{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Sample plots in Matplotlib\n",
    "\n",
    "\n",
    "Here you'll find a host of example plots with the code that\n",
    "generated them.\n",
    "\n",
    "\n",
    "Line Plot\n",
    "=========\n",
    "\n",
    "Here's how to create a line plot with text labels using\n",
    ":func:`~matplotlib.pyplot.plot`.\n",
    "\n",
    ".. figure:: ../../gallery/lines_bars_and_markers/images/sphx_glr_simple_plot_001.png\n",
    "   :target: ../../gallery/lines_bars_and_markers/simple_plot.html\n",
    "   :align: center\n",
    "   :scale: 50\n",
    "\n",
    "   Simple Plot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CoNAQrhiDEUTOtHfzqzWVXD49h6tm5QWs3MXFWVw7ZwL3rauhrrkjYOUawo+6lg7uW1fNtXMmsLg4K2DlXnleHstm5PDrNVWcae8OWLmG8MIYjCDym7JKmju6+dY1MwNe9jevnkF3Ty///WpFwMs2hA//80oFXZ5evnn1jICXfffKmTR3dPMb05MdsxiDESTqWjp4eMNBPjw3n/MmjAt4+YXZyXxy4RT+uKWWw43tAS/fEPrUNrWzakstn1g4hcLs5ICXf96EcXx4Xj4PbThoerJjFGMwgsT962ro7unlK1eWOFbHHcunES3Cb9dWOVaHIXT57doqokW4c/k0x+r4yhUleHp6uX+9iWWMRYzBCAKn2rr4Q/khPnjhREf++XnJG5fAjfMn8dS2Wo6fOetYPYbQ48SZDp7aeoQb508ib1yCY/UUZifzwQsn8ofyQ5xq63KsHkNoYgxGEHh4w0Hau3oc/efn5fbLi1G1ejSGscP966rpUeX2y0c/jHYo7lw+jfauHh7ecNDxugyhhTEYDtPR3cNjmw5x5cxcpuelOl7f5Mwkrp0zgT9uqaWlw4xmGQu0dnr445ZaPjBnApMzkxyvb3peKlfOzOWxTYfMkiFjDGMwHOb5ncdoaO3ic5dODVqdn71kKq2dHv609UjQ6jS4x5+21tLS6eGzlwSvjX3u0qk0tHaZZfbHGMZgOIiq8uAbB5mRl8qSAI6JH4q5k9MpLcjg4Q0H6el1fkdFg3v09CoPbzhIaUEGcyenB63eJcVZzMhL5cH1NQRj105DaGAMhoNsrz3N3uPN3LKkEGt7kODx2UsKOdzUzusV9UGt1xBcXq+o51BjO7csKQxqvSLCZy8pZN+JFrYdOhXUug3uYQyGgzy5pZbE2Gg+eOGEoNd99azxZKfE8fimw0Gv2xA8nth0mKzkOFbOHh/0uj944URS4mNMGxtDGIPhEG2dHp7feYxr50wgNcH/vS4CRVxMFDeUTua1fXWcOGMmWUUiJ5s7eHVfHTfOn0xcTPC/ysnxMVw/byKr3z7O6XYzxHYs4OQWrQ+KSJ2I9LtZg4h802cnvl0i0iMimfa1gyLytn1ta3/5Q52/vn2ctq4ePn7x5KETO8TNCybT06s8ubXWNQ0G53hySy09vcrNC9xrY59YUECXp5en3zzqmgZD8HDyb8nDwMqBLqrqf3h34gO+Daztsw3rcvv6fAc1OsaTW2opyklmfsHAe3Q7TUFWMkuKs3j6zSMmMBlhqCpPbz/K4qIsCrKcmww6FLMmjmPOpDSe3m5G5I0FnNyi9XXA3x1XbgaecEpLsKmsa2XroVN8bP7koAe7+3L9vHwONrazo/a0qzoMgWXnkTPUNLTx4Yvy3ZbC9XPz2XW02ez9PQYQJ/95ikghsFpVzx8kTRJwBJjm7WGISA1wClDg96p67yD5bwNuA8jLyytdtWrViLS2traSkpIyorx9+eP+Ll462M3PlyWSHj86mzxaXe3dyj+uaeeySTF8etbwt+l0SpeThKq2QOp6dE8nrx/x8D/Lk0iKHd2fktHqOtOpfK2snfdPjeWG6XGj0hJIXU4RqrpgZNqWL1++zW9Pjqo6dgCFwK4h0nwceL7PuYn2Yy6wE7jMn/pKS0t1pKxZs2bEeX3p8vRo6Q9f1i/835aAlBcIXXc8tk3nfv8l7fL0jF6QTaDulxOEqrZAtrF5P/i73vHYtoCUFwhdn3lgky756ava09M7ekE2kf45OsFItAFb1c/f9FAYJXUTfdxRqnrMfqwDngEWuKBrRGysaqShtZMbSie5LeUdPjIvn1Pt3bx+wMzJiATWVdTT1NbFh+e6747y8pGL8jl6+ixbDpp9vyMZVw2GiKQBlwN/8TmXLCKp3ufA1UC/I61CkdVvHSM1PobLp+e4LeUdLpueQ0ZSLM9sNyNZIoFnth8jIymWy0Kojb1vVh5JcdE8u8O0sUjGyWG1TwAbgRkickREPi8it4vI7T7JPgz8XVXbfM7lAetFZCewGfirqr7olM5A0uXp5cVdJ3jfrDwSYke/l3KgiI2O4oMXTuTlPSdpNgsShjUtHd38ffcJPjBnoitzLwYiKS6GlbPHs/qt42ZBwgjGyVFSN6vqBFWNVdVJqvqAqv5OVX/nk+ZhVb2pT75qVb3QPmar6o+d0hho3qhsoLnDw7Vzgj+zeyiun5dPp23QDOHLi7tO0Onp5fp5oeOO8nL9vHxaOjys2VfnthSDQ4TOX5QI4Pm3jjEuIYalJaHjKvAyb3I6BVlJPG9WFw1rntt5jCmZSVw0JXgLDfrLkuIsclLjzQq2EYwxGAGio7uHl3efZMXs8SHlKvAiIlxz/gQ2VjVypt24pcKRM+3dbKxq5JoLxrs+v6c/YqKjWDE7j7L99ZztMm6pSCT0ftnClHUVDbR0hqY7ysvK88fj6VVe3XfSbSmGEfDqvpN4etWVhQb9ZeXsCZzt7jGrJEcoxmAEiNVvHSM9KZZLpmW7LWVA5uSnMSEtwcQxwpSXdp9g/LgELpwUeu4oLwuLMklLjOWl3aaNRSLGYASAju4eXtlzkpWzxxMbHbq3NCpKWDF7PGsP1NPe5XFbjmEYtHd5WHugnhWz84iKCj13lJfY6CiuOi+PV/acpLun1205hgATur9uYcQblQ20dfVwzQWh647ycvXsPDo9vazdb1wG4cTrB+rp6O5lxfmh647ysmJ2Hs0dHsqrG92WYggwxmAEgFf2niQlPoZFRZluSxmSBYWZZCTF8qJxGYQVL+46QUZSLAsKQ7+NXTY9h8TYaOP6jECMwRglvb3KK3vruHx6DvExoTNZbyBioqN436w8XttbR5fHuAzCgS5PL6/ureN9s/KICWGXp5eE2GiWz8zhpd0nzZ7yEUbot74Q562jZ6hv6eSqWbluS/GbleePp6XTw4aqBrelGPxgQ5U1Am9lGLijvKyYPZ6G1k62Hzb7fUcSxmCMklf2nCQ6Slg+I3wMxpLibFLiY8xIljDhpd0nSImPYUlx6I7A68sVM3OJi44ybqkIwxiMUfLK3pNcXJhBelLg9gFwmoTYaC6fnsOre+vMTnwhjqryqu3yDKX1yYYiNSGWRcVZvGqWCYkojMEYBbVN7ew70cJV5+W5LWXYLJ+ZS11LJ7uPNbstxTAIu481U9fSyfKZ4dOD9XLFjBxqGtqoaWgbOrEhLDAGYxS8steaMf2+WeFnMJbNyEEEs1BciOP9fJbNCL31yYbiipnW98K0scjBGIxR8Mrek5TkplCQley2lGGTnRLPnEnpvLbffJlDmdf213HhpDSyUwK3vW6wmJKVRHFOMmtMG4sYjMEYIc0d3WyqbuKqMOxdeFk+I4cdtadpbO10W4qhH5rauthRezos3VFels/IZVN1E22dZmWBSMAYjBGyvqIBT69yZRh/ma+YmYsqrDVbt4Ykaw/UoUpYjcDryxUzc+nq6eWNSjOEOxJwcse9B0WkTkT63V5VRJaJyBkR2WEf9/hcWyki+0WkUkS+5ZTG0bB2fz3jEmKYOzl0F4IbivMnWq6O14yPOSR5bV892SnxXJCf5raUETO/MJOU+BjjlooQnOxhPAysHCLNOlWdax8/ABCRaODXwDXALOBmEZnloM5ho6qsPVDP0pKcsJh5OxBRUcKyGTm8fqAej1koLqTw9PSydn8dy2bkhPRig0MRFxPFpdOyWbOv3gzhjgCc3KL1daBpBFkXAJX2Vq1dwCrguoCKGyUHTrZyormDy6eH38iVvlwxM5fmDg9vHj7tthSDD9trT9Pc4Qlrd5SXK2bmcqK5g73HW9yWYhglMS7Xv1hEdgLHgG+o6m4gH6j1SXMEWDhQASJyG3AbQF5eHmVlZSMS0tra6nfev9VYO9bFNlZQVlY1ovr8ZTi6RkS3Ei3w8N+30j7D/8mHjusaBaGqbTi6/rS/i2gBObmPsrL9IaNrJMR1Wr3XB/5WzgeLw7+NhaouCII2VXXsAAqBXQNcGwek2M/fD1TYz28E7vdJ92ngf/2pr7S0VEfKmjVr/E77ifs26opfrB1xXcNhOLpGyk2/H/77CYaukRKq2oaja+V/v64f+90G58T4EIz79YFfrtOP/uaNYeWJhM8x2IxEG7BV/fxNd80Br6rNqtpqP38BiBWRbKwexWSfpJOweiAhQVunhy01p7gsAtxRXpZOz2bfiRbqmjvclmIA6lo62Hu8ObLaWEk222tP09Jh9pMPZ1wzGCIyXuyd7EVkga2lEdgClIjIVBGJA24CnnNLZ1/Kqxvp6umNiPiFl8tKrPey3gx9DAm8Q1C9n0sksLQkh55eZWOV2VQpnHFyWO0TwEZghogcEZHPi8jtInK7neQGYJcdw/glcJPdQ/IAdwEvAXuBJ9WKbYQEaw/UkxgbzfzCDLelBIxZE8aRmRzH+gpjMEKBdRUNZCTFMnviOLelBIyLCtJJios2f0rCHMeC3qp68xDXfwX8aoBrLwAvOKFrtKw9UM+S4qyw2CzJX6KihEumZfN6RQOqit3xM7iAqrKuooFLS8J7OG1f4mOiWVSUxTrzpySsCd9JBC5wsKGNQ43tXB6GC8ENxdKSbBpaO9l3wgx9dJP9J1uob+lkaUn47H3hL0tLsqlpaKO2qd1tKYYRYgzGMFhnd6eXRpBv2Yv3B2pdhVkmxE3WHfC2scg0GIDpZYQxxmAMgw2VDeSnJ1KYleS2lIAzIS2RktwU82V2mXWVDUzLTWFCWqLbUgJOcU4KE9ISWF9p/pSEK8Zg+Elvr7KxupElxVkR6+O/tCSbzTVNdHT3uC1lTNLR3cOm6saI7F0AiAhLS7JZX9FAT69ZJiQcMQbDT/Ycb+Z0ezeXTIvMLzNYwzg7Pb1sOTiSFV0Mo2XrwVN0enojajhtX5aW5NDc4eGtI2YpmnDEGAw/8Y6NX1Kc5bIS51hYlElstBi3lEusq6wnNlpYWJTpthTHuGRaNiImjhGuGIPhJ+srGyjJTSF3XILbUhwjKS6G0oIM82V2ifUVDZQWZJAU5/YSb86RmRzH+RPTzJyfMMUYDD/o9PSw5WBTRLujvFw6LZu9x5tpautyW8qY4nR7F3uON3NJceS3sUumZbO99hTtXWYXvnDDGAw/2H74NB3dvRHtjvKy2H6P5dVmCYdgsrmmCVVYNEbaWHePsvXgKbelGIaJMRh+sKGygSiBhUWR/2WeM8lawmFDlXEZBJON1Y0kxEYxZ1L47q7nLxcXZhATJWww60qFHcZg+MEbVY3MmZROWmKs21IcJzY6igVTM80icUGmvLqJ0oKMiFpyZiCS4mKYNyWdjaYXG3YYgzEErZ0edtae5pJpkd+78LKkOIuq+jZOmuXOg8Lp9i72nWhm0dSx08YWF2fz9pHTNJvlzsMKYzCGYHNNI55eHRPBSC+Li6z3anoZwWGTHb9YPAbiF14WF2XRq7C52sz5CScGNRgiMklEviEifxGRLSLyuoj8RkSuFZExYWzWVzQSHxPFRQWRs5z5UMyaOI5xCTEmjhEkyt+JX6S7LSVozJuSTnxMlIljhBkDDvgWkYew9tdeDfwMqAMSgOnASuA7IvItVX09GELdYkNVA/MLM0iIjXzfspfoKGFRUZbxMQeJjVWNzC/IJC5mTPwHAyDB3lPGtLHwYrAW+l+qerWq/lJVN6hqparuUtWnVfUfgGUMsnWqiDwoInUismuA658UkbfsY4OIXOhz7aCIvC0iO0Rk60jf3GjxLve9ZAy5o7wsKc6itumsWYraYU61dbHvRAuLInh290AsKTZzfsKNwQzGShGZNNBFVe1S1cpB8j+M1RMZiBrgclWdA/wQuLfP9eWqOldV5w9ShqN4u8tjYcJeXxYXmzhGMNhUY/nwF42BIdt98b5nM+cnfBjMYOQDG+24xZdFZFi/mrarasCIlt1r8c7cKQcGNE5usaGygdSEGC7Ij/yx8X2ZnpdCVnKciWM4THl1I4mx0WMqfuFlzqQ0ks2cn7BCVAdeZlisdbwvA24CrgN2Ak8Az6jqkFuziUghsFpVzx8i3TeAmar6Bft1DXAKUOD3qtq39+Gb9zbgNoC8vLzSVatWDSWrX1pbW0lJSXnPuW+ubWdSahT/eJF760f1pytY/GZHBwdO9fKLZYnnLOnqE3FjAAAgAElEQVTupq6hCFVt/en67htnGRcH37zYvf0v3LxfP9/WQX17Lz9deu4eM+H0OYYKI9G2fPnybX57clTVrwOIBlYA24F2P/MUAruGSLMc2Atk+ZybaD/mYhmpy/ypr7S0VEfKmjVr3vP6yKl2Lbh7tT6wrnrEZQaCvrqCyWPlh7Tg7tVaWddyzjU3dQ1FqGrrq6uptVML7l6tv3qtwh1BNm7er3vXVmnB3av1xJmz51wLl88xlBiJNmCr+mkH/BqWISIXAD8Afg10Af/ilzUautw5wP3Adar6jiNTVY/Zj3XAM8CCQNQ3HDbZftWx6Fv24p0XYIY+OsOmGm8bG3sBby/eNmZiZeHBgAZDREpE5Lsisgd4HGgHrlbVhar636OtWESmAE8Dn1bVAz7nk0Uk1fscuBrod6SVk5RXN5KWGMvM8anBrjpkKMxKYkJaAhuNj9kRyqubSIyN5oL8sRe/8HLehHGkJcaaOEaYMNjC+y9hxSs+rqpvD7dgEXkCa+httogcAb4HxAKo6u+Ae4As4De2f9yjlh8tD3jGPhcDPK6qLw63/tGyqaaJBVMziYqKzO1Y/UFEWFycRdn+enp7dUzfCycor25kfmHGmJp/0Rdrzk+mmY8RJgxoMFS1yPe1iIzzTa+qg87pV9Wbh7j+BeAL/ZyvBi48N0fwOHb6LIca2/nM4kI3ZYQEi4uyePrNo+w/2cJ5E8a5LSdiaLLnX3zwwoluS3GdxUVZvLT7JLVN7UzOPDf4bQgdhvxrIyJfEpGTwFvANvtwbTJdMDC+5XdZYs9BMXGMwLK5xsTIvHjbmIljhD7+9IW/AcxW1UJVnWofRUPmCmM2VTcxLiGGmePNP+r89ESmZCaZyVUBZmOVd/7F2Jvj05eSXGvOj2ljoY8/BqMKK+A9ZiivbmTB1Cyijc8esHpam2ua6O0deM6OYXiUVzcxvzCD2OixG7/wImKtXVZe3egdVm8IUfxprd8GNojI70Xkl97DaWFucfzMWQ42tht3lA+LirI4c7abvSea3ZYSETS2drL/ZItxR/mwqCiTY2c6qG0667YUwyAMNkrKy++B14C3gV5n5bjPpuqxu7bPQLy75k8TsycaF8po2TyG148aCN91paZkmcB3qOKPwfCo6j85riRE2FTTSGpCjBkR5MPE9EQKsqw4xucvneq2nLCnvLqRpDgTv/BlWm4K2SlxbKxu5GMXT3ZbjmEA/HFJrRGR20Rkgohkeg/HlblEeXUTC6dmmvhFHxZNzWJTdSM9Jo4xaqz4RaaJX/ggIiw0cYyQx58W+wnsOAYRPqz2xJkOahrajKugHxYVZ9Lc4WHvcRPHGA3vxi8i9j/XiFlUlMXxMx0cNnuwhCxDuqRUdcz4ILzzLxZONQajL957Ul7dyPljcLn3QDGW978YisW2ES2vbqQgK9llNYb+GGwtqUsHyygi40Rk0GXLw43y6iZS42OYNdHEL/rybhxj0An+hiHwxi/G4h4rQ1GcY8UxTBsLXQbrYXxURP4deBHLDVWPtaf3NKwlyQuArzuuMIhsqm5kgYlfDMjioixeePu4iWOMAmv9KBO/6A9vHGNjlYljhCoDtlpV/RpwLXAcuBFrG9V/AkqwNjW6TFW3BEVlEDjV0Uu1iV8MyqKiLBPHGAXNncqBk60sNm1sQBYXZXGiuYNDjSaOEYoMGsNQawvV++wjotnfZE0xWWiCkQOy0MfHPM1lLeHIvlM9gFmjbDB852OMd1mL4VxMv9hm36keK35h5l8MyIS0RAqzzLpSI2VfUw/JcdFm0MAgFOckk50Sb9pYiGIMhs2+ph4unppJjPEtD8qioiw21TTRa3zMw2ZfU4+JXwyBta5UJuXVTSaOEYKYlgvUNXdwok1ZONW4CoZicXEWLR0eDjdH/CoxAaWhtZNjrWpiZH6wyI5j1LUbgxFq+LMfRpK9Vet99usSEfmAP4WLyIMiUici/W6xKha/FJFKEXlLRC7yuXaLiFTYxy3+vqGRYMbG+493Psa+JmMwhsO7a5SZPyVD4d3ne29Tj8tKDH3xp4fxENAJLLZfHwF+5Gf5DwMrB7l+DdaoqxLgNuC3APbSI98DFgILgO+JSIafdQ6b8upGEqJhtpl/MSTj0xKYmp1svszDxNvGzPyLoSnKTiYnNZ59po2FHP4YjGJV/XegG0BVzwJ+TVRQ1deBwWbhXAc8ohblQLqITABWAC+rapM9UutlBjc8o6K8upHpmdEmfuEni4oyOXCqx8zHGAYbqxuZnmHamD9498fY19Rr4hghhj+r1XaJSCKgACJSjNXjCAT5QK3P6yP2uYHOn4OI3IbVOyEvL4+ysrJhCejqUejqYFpmz7DzBoPW1taQ0zWuw8NZDzz6/GsUpkW7LeccQu2enelUKuvaua5QQ0qXl1C7XwCZ3d2c7lT++MIaxieHlpENxfvlxWlt/hiM72HN9p4sIo8BlwC3Bqj+/noqOsj5c0+q3gvcCzB//nxdtmzZsEVcfSWUlZUxkrxOE4q6zmvu4PdvvUp3xlSWXRZ6u/WG2j1b/dYxYDsXjk8MKV1eQu1+AUyub+X/9qxFc6axbMEUt+W8h1C8X16c1jak6VbVl4GPYBmJJ4D5qloWoPqPAL6L308Cjg1y3hAC5I1LYHySmLHyflJe3UhKfAwF40Lrn3IoU5SdTFq8sLHKtLFQwp9RUhdhrRt1HOtHe4qIFIuIP72ToXgO+Iw9WmoRcEZVjwMvAVeLSIYd7L7aPmcIEWZmRrO5psnEMfygvLqJiwszzBplw0BEOC8zyuyPEWL485fnN0A5ltvnPmAjsAo4ICJXD5ZRRJ6w088QkSMi8nkRuV1EbreTvABUA5V22XcAqGoT1tpVW+zjB/Y5Q4gwMzOalk4Pu4+dcVtKSFPX0kFlXasZsj0CZmZGU9fSSU1Dm9tSDDb+9BIOAp9X1d0AIjIL+CbWD/rTwN8HyqiqNw9WsFp/He4c4NqDwIN+6DO4wMxM679GeXUjcyalu6wmdPHdI/5UVe0QqQ2+zMy0BlSUVzdRlJPisprQ5fUD9RxuaufmIMR6/OlhzPQaCwBV3QPMU9Vq52QZQp30hCiKcpLN3gVD4I1fmDk+wycvSchNNetKDcUTmw/z27KqoLg8/TEY+0XktyJyuX38BssdFY89N8MwNllUlMWWmiY8PWbW90CUVzdycWGGmX8xArzzMTaaOMaAqCqbapqCtsq2P634VqwYw1eBr2HFHG7FMhbLnRJmCH0WFWXR0ulhj9kfo1/qWjqoqjd7rIyGxcVZ1Ld0Um3iGP1SUddKU1tX0NqYP3t6nwX+yz760hpwRYawYdHUd/fHMHGMc/HGL7xrIxmGj+/+GMUmjnEOXnddsDbl8mdYbYmIPCUie0Sk2nsEQ5whtMkdl0BRTrIZKz8AG6sbzR4ro6QwK4m8cfEmVjYAm6qbmJiWwKSMxKDU5+/ig78FPFguqEeAR50UZQgfFhdlseXgKRPH6Ify6kazx8oo8cYxzHyMc7HiF40sKspCJDhzfPxpyYmq+iogqnpIVf8VuMJZWYZwYVFRFq2dHnYfM3EMX+qaO6iubzPLmQeARUVWHKOq3sQxfKmqb6WhtSuo20r7YzA6RCQKqBCRu0Tkw0Cuw7oMYYLvPt+Gdyk3e6wEDN84huFdNlYHv435YzC+CiQBXwFKgU8Bn3FSlCF8yE1NoDgn2XyZ+1Bu4hcBozArifHjEkwb60N5dSMT0hKYkpkUtDr9MRiFqtqqqkdU9bOq+lEgtJaPNLjKIhPHOIfyqkYWmPhFQDD7fJ+LqrKpuomFUzODFr8A/wzGt/08ZxijLC624hi7TBwDgJPNHVQ3mPkXgWRRURYNrSaO4aWqvo2G1s6gt7EB52GIyDXA+4F8Efmlz6VxWCOmDAbg3X2+y6sbmTvZzMfwuk6MwQgc3nu5sbqRablmPsamGnfa2GA9jGPANqDDfvQez2FtoWowAJCTGs+03BTjY7Z5J35h1o8KGAUmjvEeyqubyBsXT0FW8OIXMEgPQ1V3AjtF5A+qanoUhkFZVJTJM28exdPTO+b99hurGllYlGn2vwggIsLi4izWVdSjqkH124caqsrGqgYunZYd9Psw4DdbRN4WkbeAN0Xkrb5HEDUawoBFRVm0dfWM+TjG0dNnOdjYzuLibLelRByLijJpaO2iqn5sr0hUUWfNv1jiQhsbbC2pDwRNhSHsecfHXDW24xjeZVKWmPWjAs67cYwmpuWmuqzGPbxtzI01ygbsYdizug+p6iGsOMYF9nHWPjckIrJSRPaLSKWIfKuf678QkR32cUBETvtc6/G59tzw35ohmGSnxFNi4hhsrGokMzmOGXlj9wfNKaZkJjEhzcQxNlQ1MDkzkclBnH/hxZ/FBz8GbAZuBD4GbBKRG/zIFw38GrgGmAXcbO/W9w6q+jVVnauqc4H/xdrBz8tZ7zVV/ZDf78jgGouKsth6sInuMTofw+tbXlSUSZSJXwQc77pSm8bwulI9vUp5dVPQVqftiz/Rye8AF6vqLar6GWAB8F0/8i0AKlW1WlW7sPYBv26Q9DcDT/hRriFEeSeOcXRs7vN9qLGdY2c6TPzCQbxxjMq6sRnH2Hu8mTNnu12JX4B/e3pHqWqdz+tG/DM0+YDvJsZHgIX9JRSRAmAq8JrP6QQR2Yo15+PfVPXZAfLeBtwGkJeXR1lZmR/SzqW1tXXEeZ0knHT1dFr/+h5/ZQtniuJcUGXh1j0rq7U2oIxpqKKsrOac6+H0WYYC/emSdqv3+shL5Vw5JdYFVe7er7/VWG1MT+6nrKzinOuOa1PVQQ/gP4CXsHbZuxX4G/AzP/LdCNzv8/rTwP8OkPbuvteAifZjEXAQKB6qztLSUh0pa9asGXFeJwk3Xe/7eZl+5oFNwRXTB7fu2Z2PbdMFP35Ze3t7+70ebp+l2/Snq7e3Vxf/5BW94w/bgi/Ixs37deuDm/SK/xy4/pFoA7bqEL+t3mPInoKqfhP4PTAHuBC4V1Xv9sMWHQEm+7yehDUZsD9uoo87SlWP2Y/VQBkwz486DS5jrSs19uIYqkp5dSOLg7g3wVhkLO+P0d3Ty+aaJld3cPQn6P01YLOq/pNaQepn/Cx7C1AiIlNFJA7LKJwz2klEZgAZwEafcxkiEm8/zwYuAfb4Wa/BRRYVZdHe1cPbYyyO4ebY+LHGoqIsGtu6qBhjcYy3jpyhravH1TbmTyxiHPCSiKwTkTtFJM+fgtWaHX4XljtrL/Ckqu4WkR+IiO+op5uBVfrevwvnAVtFZCewBiuGYQxGGLBg6tjcH2NDZQNg9u8OBmN1f4xQWKNsyKC3qn4f+L6IzAE+DqwVkSOqepUfeV8AXuhz7p4+r/+1n3wbsOZ8GMKM7JR4puelUF7dxB3L3FYTPDZUNbo2Nn6sMTkzkfz0RMqrG/nM4kK35QSNDVUNnDdhHJnJ7g0oGc6iP3XACaxRUmbHPcOALB5j8zGssfGNro2NH2uICAvH2P4YHd09bD14yvU25k8M48siUga8CmQDX1TVOU4LM4QvYy2Osfd4M80dHhO/CCKLirJoGkNxjO2HT9Pp6XV9yRl/ehgFwFdVdbaqfs/EEgxD4Y1jeNe8iXQ2VJn4RbBZ7LN22VhgY3UjUQILijJd1eHPsNpvqeqOYIgxRAZZKfHMyEsdM0HJDVWNFOckkzcuwW0pY4ZJGe/GMcYCG6sauGBSOuMS3Jms6GVsb1xgcIxFRZlsPXgq4uMYoTA2fizijWNsqmmitzey4xjtXR62Hz7tevwCjMEwOMTi4izOdvews/b00InDmJ21p2l3eWz8WGWxHcfYf7LFbSmOsrmmCU+vuh6/AGMwDA6xuCibKIF1FQ1uS3GUdRUNiJj9L9zg0hLLSK+P8Da2vqKBuJgoLi50N34BxmAYHCItKZY5k9JZV1HvthRHWVdRz5xJ6aQnuTc2fqwyIS2RabkpvB7xbayBBYWZJMZFuy3FGAyDc1xWks2O2tOcOdvtthRHOHO2mx21p7msxLij3GJpSTaba5ro6O5xW4ojnGzuYP/JFpaGSBszBsPgGEun59CrkTv0cWNVI70Kl04LjS/zWOSykhw6Pb1sPXjKbSmO4HW3XWoMhiHSmTs5nZT4mIh1S62rqCc5Lpp5UzLcljJmWViUSWy0RHQby06J47zx49yWAhiDYXCQ2OgoFhVlRWzge11FA4uLs4iLMV8jt0iKi6G0IIPXI7CN9fYq6ysbuHRadshs+WtausFRLpuezeGmdg41trktJaAcamzjcFM7S0ty3JYy5llaksPe483Ut3S6LSWg7DvRQkNrV0i1MWMwDI7i9e9HWi9jXYj5lscy3oDwG5WR1sYsN1sotTFjMAyOMjU7mfz0xIjzMa+rqCc/PZGi7GS3pYx5Zk9MIyMpNuKG166raGBGXmpILTnjqMEQkZUisl9EKkXkW/1cv1VE6kVkh318wefaLSJSYR+3OKnT4BwiwmXTs9lQ1YgnQpYJ8fT0sqGqkaUl2WY71hAgOkq4ZFo26ysaIma5847uHjYfbAqZ4bReHDMYIhIN/Bq4BpgF3Cwis/pJ+kdVnWsf99t5M4HvAQuBBcD3RMQMRQlTlpbk0NLhYeeRyFgmZOeR07R0eELKVTDWuawkh7qWzohZJmRTTRNdnt6Qa2NO9jAWAJWqWq2qXcAq4Do/864AXlbVJlU9BbwMrHRIp8FhLpmWTXSUsGZfZLgM1uyrJzpKWDotdIKRY53LplufReS0sToSYqNc3Y61P4bconUU5AO1Pq+PYPUY+vJREbkMOAB8TVVrB8ib318lInIbcBtAXl4eZWVlIxLb2to64rxOEim6pqUJz22tZn78cedE2Th9z57bepbiNGH75jeGlS9SPstgMVxdU1KjeGbTAc57z09H4HH6fqkqL+w4y4z0KMrfWDesvI5/lqrqyAHcCNzv8/rTwP/2SZMFxNvPbwdes59/E/h/Pum+C3x9qDpLS0t1pKxZs2bEeZ0kUnT9Zk2lFty9Wo+fPuuMIB+cvGfHT5/VgrtX66/XVAw7b6R8lsFiuLr+/cW9WvTtv+rpti5nBNk4fb8q61q04O7V+siGmmHnHYk2YKv6+bvupEvqCDDZ5/Uk4JhvAlVtVFXv4On7gFJ/8xrCiytmWtvAl+2vc1nJ6PDq974fQ+hwxcxceno17EdLrdlntbHlIdjGnDQYW4ASEZkqInHATcBzvglEZILPyw8Be+3nLwFXi0iGHey+2j5nCFOm56WQn57Ia/vC22C8tq+OiWkJzMhLdVuKoQ9zJ2eQkRT7zg9uuLJmfx3T81KYlJHktpRzcMxgqKoHuAvrh34v8KSq7haRH4jIh+xkXxGR3SKyE/gKcKudtwn4IZbR2QL8wD5nCFNEhOUzc1hf2UCnJzxXFu309LC+soHlM3PNcNoQJDpKuHx6DmUH6ukJ0134Wjs9bK5pCsneBTg8D0NVX1DV6aparKo/ts/do6rP2c+/raqzVfVCVV2uqvt88j6oqtPs4yEndRqCw/IZubR39bC5Jjxt/+aaJtq7elg+IzS/zAbLjdPU1hW2Q7jXV9TT3aMh28bMTG9D0FhSnE18TFTYDn1cs6+euJgolkwLraGOhne5fHoOUQJlYeqWWrOvntQEa0HFUMQYDEPQSIyLZnFxFmvCNPC9Zn8di4uySIpzcjS6YTSkJ8Vx0ZQMXgvDNqaqrNlfx2XTc4iNDs2f5tBUZYhYls/IpaahjZqG8Fq91qt5+QwzWS/UWT4zl11Hm6lr7nBbyrDYfayZupbOkHVHgTEYhiDjHY76yp6TLisZHl69V56X57ISw1C808b2hlcv4+U9JxGBZSH8p8QYDENQmZyZxOyJ43hx9wm3pQyLF3efYNaEcUzODL2hjob3MnN8KgVZSWHXxl7afYKLCzPJTol3W8qAGINhCDorZ49n26FTYeMyqGvuYNuhU6w8f7zbUgx+ICKsnD2eDZUNnDnb7bYcv6hpaGPfiRZWzg7tNmYMhiHoeH94XwoTt5RXpzEY4cOK88fj6VVe2xcmbczuDa0I8TZmDIYh6EzLTaEoJ5mXdoWHy+ClXScoyk6mJDfFbSkGP5k7KZ28cfG8GCZt7MVdJ5gzKY389ES3pQyKMRiGoON1GWysbuR0e5fbcgbldHsX5dWNrDh/vJndHUZERQkrZo9n7YF6znaF9soCx8+cZUftaVaEuDsKjMEwuMSK2ePp6dWQH8ny6t46PL0aFl9mw3tZMXs8Hd29rD0Q2hNF/77bcpuFQxszBsPgCnMmpTEhLSHkXQYv7j7BhLQE5uSnuS3FMEwWTM0kPSn2nfhAqPLirhNMy01hWhi4PI3BMLiCiOUyeL2inrZOj9ty+qWt08PrB+pZMXs8UVHGHRVuxEZHcdV5ebyy9yRdntDcT76prYtNNY0hPzrKizEYBtdYef54ujy9Ibvkedn+ejo9vVw920zWC1dWzh5PS4eHN6oa3JbSLy/vOUGvhoc7CozBMLjIxYWZ5KbG85cdobk31rM7jpKbGs/CqWaxwXBl6fRsxiXE8FyotrHtxyjMSuL8/HFuS/ELYzAMrhEdJVw3dyJl++toagut0VKn2roo21/HdXMnEm3cUWFLfEw0186ZyIu7ToSc6/PY6bOU1zRy/bz8sBmBZwyGwVU+PG8Snl7lr28fd1vKe/jr28fp7lGun5fvthTDKPnwvHzOdvfwcohNFH1u5zFULX3hgqMGQ0RWish+EakUkW/1c/2fRGSPiLwlIq+KSIHPtR4R2WEfz/XNa4gMzpuQyoy8VJ7dftRtKe/hme1HmZ6XwqwJ4eEqMAzM/IIM8tMTeTrE2tiz249y0ZR0CrKS3ZbiN44ZDBGJBn4NXAPMAm4WkVl9km0H5qvqHOAp4N99rp1V1bn28SEMEYmIcP28fLYdOsXhxna35QBwuLGdbYdOhZWrwDAwUVHC9fMmsr6inrqW0Fi/bO/xZvadaAmr3gU428NYAFSqarWqdgGrgOt8E6jqGlX1/kqUA5Mc1GMIUa6bOxGwgsyhgFfH9XPD68tsGJgPz8unV+H5naHh+nx2+1FiooRr50x0W8qwEFVnNksXkRuAlar6Bfv1p4GFqnrXAOl/BZxQ1R/Zrz3ADsAD/JuqPjtAvtuA2wDy8vJKV61aNSK9ra2tpKSE3sSZsaLr3zaf5XSH8tOliaP+Vz8abarKt9edJT1B+NaCwK7rM1Y+y0ARaF3/uuGs9bhkdJ/raHX1qvL1srMUjIviq6UJo9LSl5FoW758+TZVne9XYlV15ABuBO73ef1p4H8HSPsprB5GvM+5ifZjEXAQKB6qztLSUh0pa9asGXFeJxkrulZtPqQFd6/W7YdPjbqs0WjbcfiUFty9WldtPjRqHX0ZK59loAi0rvvXVWvB3au14mTzqMoZra43Kuq14O7V+vzOo6Mqpz9Gog3Yqn7+rjvpkjoCTPZ5PQk4ZzC0iFwFfAf4kKp2es+r6jH7sRooA+Y5qNXgMtdcMIH4mCie3Frrqo4/bq0lPiaKledPcFWHIfB86MKJxEQJT2494qqOP26tJTUhhqvCcPdGJw3GFqBERKaKSBxwE/Ce0U4iMg/4PZaxqPM5nyEi8fbzbOASYI+DWg0uMy4hlg/Mmchfth91bbx8W6eHv2w/ygfmTCQtMdYVDQbnyEmN56rz8nhq2xE6Pe6sYNvU1sXf3j7BR+blkxAb7YqG0eCYwVBVD3AX8BKwF3hSVXeLyA9ExDvq6T+AFOBPfYbPngdsFZGdwBqsGIYxGBHOJxZOoa2rh+d2ujMr97mdx2jr6uETC6e4Ur/BeT6xcApNbV28tNudORl/3naErp5ePrGwYOjEIUiMk4Wr6gvAC33O3ePz/KoB8m0ALnBSmyH0uGhKOjPHp/LYpkPcdPHkoA5pVVUe33SYmeNTuWhKetDqNQSXS6dlMzkzkcfKD/GhC4M7Qqm3V3li82FKCzKYMT41qHUHCjPT2xAyiAifXFTArqPNvHn4VFDrfvPwKd4+eoZPLpxi5l5EMFFRwicWFLCppom9x5uDWve6ygaqG9r4ZBj3YI3BMIQUH70on3EJMTy4/mBQ631w/UHGJcTwkYvMVKBI5+YFk0mMjeahN2qCWu+D62vISY3nA2E298IXYzAMIUVSXAw3L5zC33Yd58ip4Mz8PnKqnb/tOs7NC6eQHO+ol9YQAqQnxfHR0nye3XGMhtbOoTMEgMq6FtYeqOcziwqIiwnfn93wVW6IWG5ZXIiIBK2X8dAbBxERPrO4MCj1Gdzn1iVT6fL08siGg0Gp7/51NcTHRIX9gApjMAwhx8T0RK6fm8/jmw/R6PA/wMbWTh7bdIjr5k4kPz2wM7sNocu03BRWzM7j4Q0Hae7odrSuo6fP8uc3j3DTxZPJSol3tC6nMQbDEJLcsbyYTk8vD6x31s/8wPoaOj293LFsmqP1GEKPu5aX0Nzh4dGNhxyt5961VajCbZcXO1pPMDAGwxCSFOek8P4LJvDIxkOOba7U1NbFIxsP8f7zJzAtN/TWUjI4ywWT0rh8eg4PrK+hxaFexokzHazaUstHL5oUET1YYzAMIctXryyhvcvDr16rdKT8X71WSXuXh3+8qsSR8g2hzz+9bzpNbV3c93q1I+X/4uUDqMJdV0RGD9YYDEPIUpKXyo2lk3m0/CC1TYEdMVXb1M6j5Qe5oXQS0/PCcxKVYfRcODmday+YwH3ragK+V0bFyRb+tK2WTy0qYHJmUkDLdgtjMAwhzdfeN53oKOHHf90b0HJ/8sJeokT42vumB7RcQ/jxzRUz6O7p5d9f3B+wMlWVH6zeQ3JcTMT0LsAYDEOIMz4tgX+4ooQXd5/gtX2BWf/ntX0n+duuE3zlyhImpIW/X9kwOgqzk/nC0iKe2naETdWNASnz+beOs66iga9fPZ3M5LiAlBkKGINhCJDp0+8AAAyFSURBVHm+uLSIktwUvvvsblpHuZJta6eHe/6ym5LcFL64tChACg3hzj9eWcKkjET+5Zm36ege3Uq2p9u7+OHqPcyZlManI2xujzEYhpAnLiaKf/voBRw/c5Z7nt01qrLu+csujp0+y08/ckFYz7g1BJbEuGh+8uELqKpv4ycvjNz9qar881Nvcbq9i598+AKioyJrXTLzjTGEBaUFmXzlyhKe3n6UP41wk6Wnth3h6TeP8pUrS5hfmBlghYZw57LpOXzh0qk8svEQf3t7ZHt/P7LxEH/fc5K7V87k/Py0ACt0H2MwDGHDP1xRwpLiLP7lmbfZUNUwrLwbqhr49tNvsbgoi7uWR04Q0hBY/nnlTOZOTudrT+5g+zBXTH5170m+//xurpyZy+cumeqQQncxBsMQNkRHCb/9VClTs5P50iPb2FzT5Fe+LQeb+NKj2yjMSuZ3nyolJto0e0P/xMVEcf8t88lNTeBzD2/hrSOn/cq3rqKeux7fzuyJafzy5nlERZgryouj3xwRWSki+0WkUkS+1c/1eBH5o319k4gU+lz7tn1+v4iscFKnIXxIS4zl4c8uIGdcPJ96YBN/3nYEax/7c1FVnn7zCJ+8fxM5qfE8/LkFpCWZrVcNg5OdEs8jn1tAcnwMN91bzguDuKdUlcc2HeKzD22hICuJB2+9OKJXPHbMYIhINPBr4BpgFnCziMzqk+zzwClVnQb8AviZnXcW1h7gs4GVwG/s8gwGJqYn8ufblzB3cjpf/9NObnloC+srGujttQxHrypvVDZwy0Nb+KcndzJ3Ujp/vn1JRCzNYAgOhdnJPH3HEkpyU7jjsTf5wv9tpby68Z021tOrlO2v4+b7yvnOM7tYXJzFk7cvJic1vBcXHAonTeECoFJVqwFEZBVwHeC7N/d1wL/az58CfiXWdmfXAatUtROoEZFKu7yNDuo1hBEZyXE88cVFPLrxIL94pYJPPbCJ+JgoslPiqWs+S3fvJtISY/neB2fxmcWFETdaxeA8uakJPPXlJTywvoZfv1bJK3tPkhAbRXK00vLyi3T19JKZHMdPP3IBH58/OWLdUL7IQN35URcscgOwUlW/YL/+NLBQVe/ySbPLTnPEfl0FLMQyIuWq+gf7/APA31T1qX7quQ24DSAvL6901apVI9Lb2tpKSkroLUBndA1NV4+yo66H6jO9nOnsJSnKw/TsBOblRhMXHTpf4lC6Z74YXUPT2aNsO9nDoeYemtq6yU6Jozgtirm50cSEkKEYyT1bvnz5NlWd709aJ3sY/d3FvtZpoDT+5LVOqt4L3Aswf/58XbZs2TAkvktZWRkjzeskRpd/XO3zPNS0eTG6hkeo6fIGUkNNly9Oa3My6H0EmOzzehJwbKA0IhIDpAFNfuY1GAwGQxBx0mBsAUpEZKqIxGEFsZ/rk+Y54Bb7+Q3Aa2r5yJ4DbrJHUU0FSoDNDmo1GAwGwxA45pJSVY+I3AW8BEQDD6rqbhH5AbBVVZ8DHgAetYPaTVhGBTvdk1gBcg9wp6qOboEXg8FgMIwKRwcMq+oLwAt9zt3j87wDuHGAvD8GfuykPoPBYDD4j5nyajAYDAa/MAbDYDAYDH5hDIbBYDAY/MIYDIPBYDD4hWMzvd1AROqBQyPMng0Mb83s4GB0DZ9Q1WZ0DQ+ja/iMRFuBqub4kzCiDMZoEJGt/k6PDyZG1/AJVW1G1/AwuoaP09qMS8pgMBgMfmEMhsFgMBj8whiMd7nXbQEDYHQNn1DVZnQND6Nr+DiqzcQwDAaDweAXpodhMBgMBr8wBsNgMBgMfhHxBkNEVsr/b+/cY6yqrjj8/dpaCNYoSJqiFRHUGrHy0NgWadXWBMUoNo0JrU2l0jTUatoYTTAkxJjUmvBHH6nGqGmsicEHPiK2GqGiGOhgqAVGS0EYjDWYolYRopnWuvrHXkc2x3tnjsw9504m60tuZp/9OOc36647++yz76wlbZO0Q9LiFu2jJN3v7RskTcrabvD6bZLmlMc2oO1aSX+XtEXSnyUdn7X9T9Imf5XDxteta4GkN7Lr/yhru0LSy/66ojy2Zl2/yjRtl/RO1lanvX4vaY9nkGzVLkm/dd1bJM3M2uq012C6Lnc9WyStlzQta3tFUq/ba2PDus6VtDd7v5ZmbQP6QM26rs80veg+Nc7b6rTXcZLWSNoq6SVJP2vRpxkfM7MR+yKFVd8JTAY+C2wGTi31uQq43cvzgfu9fKr3HwWc4Of5dMPazgPGePknhTY/3t9Fmy0Aftdi7Digz3+O9fLYpnSV+l9DCqlfq7383N8AZgIvtmmfCzxByiT5VWBD3faqqGtWcT3gwkKXH78CjO+Svc4FHh+qD3RaV6nvxaT8PU3YawIw08tHANtbfCYb8bGRvsI4C9hhZn1m9h/gPmBeqc884A9eXgF8S5K8/j4z6zezXcAOP19j2sxsjZm954c9pMyDdVPFZu2YA6wys3+b2dvAKuCCLun6LrC8Q9ceEDNbS8rn0o55wD2W6AGOkjSBeu01qC4zW+/Xheb8q4q92jEU3+y0rib963Uze8HL+4CtwLGlbo342EifMI4F/pkdv8bHDf1RHzP7ANgLHF1xbN3achaS7iAKRkvaKKlH0qVd0PUdX/qukFSk063TZpXP7Y/uTgCezqrrslcV2mmv28c+CWX/MuApSX+V9OMu6PmapM2SnpA01euGhb0kjSH90X0oq27EXkqPzGcAG0pNjfhYrQmUhgFqUVf+HnG7PlXGDoXK55f0feBM4JyseqKZ7ZY0GXhaUq+Z7WxI10pguZn1S1pEWqF9s+LYOnUVzAdW2MFZGuuyVxW65WOVkHQeacKYnVWf7fb6PLBK0j/8DrwJXiDFN9ovaS7wKClN87CwF+lx1Dozy1cjtdtL0udIk9TPzezdcnOLIR33sZG+wngNOC47/iKwu10fSZ8BjiQtS6uMrVsbks4HlgCXmFl/UW9mu/1nH/AM6a6jEV1m9lam5U7gjKpj69SVMZ/S44Ia7VWFdtrr9rFBkXQ6cBcwz8zeKuoze+0BHqGzj2MHxMzeNbP9Xv4TcJik8QwDezkD+Vct9pJ0GGmyuNfMHm7RpRkfq2OTZri8SCuoPtLjiWKTbGqpz085eNP7AS9P5eBN7z46u+ldRdsM0ibfSaX6scAoL48HXqZDm38VdU3Iyt8GeuzABtsu1zfWy+Oa0uX9vkTagFQT9squMYn2m7gXcfCG5PN126uiromkvblZpfrDgSOy8nrgggZ1faF4/0h/eF9121Xygbp0eXtxQ3l4U/by3/0e4NcD9GnExzpm6OH6In17YDvpD+8Sr7uJdMcOMBp40D84zwOTs7FLfNw24MIuaFsN/AvY5K/HvH4W0OsfmF5gYcO6fgm85NdfA5ySjb3SbbkD+GGTuvz4RuCW0ri67bUceB34L+mObiGwCFjk7QJudd29wJkN2WswXXcBb2f+tdHrJ7utNvv7vKRhXVdn/tVDNqG18oGmdHmfBaQvw+Tj6rbXbNJjpC3ZezW3Gz4WoUGCIAiCSoz0PYwgCIKgQ8SEEQRBEFQiJowgCIKgEjFhBEEQBJWICSMIgiCoREwYQdAGSUdJuio7PkbSipqudWkelbVF+5cl3V3HtYOgKvG12iBog8ftedzMTmvgWutJ/0/y5gB9VgNXmtmrdesJglbECiMI2nMLMMVzHCyTNKnIlaCUE+RRSSsl7ZJ0tVL+kr95gMMiT8IUSU96ULrnJJ1Svoikk4H+YrKQdJnnW9gsKY9HtJIUjSAIukJMGEHQnsXATjObbmbXt2g/DfgeKXzFL4D3zGwG8BfgB97nDuAaMzsDuA64rcV5ziYF3CtYCswxs2nAJVn9RuDrQ/h9gmBIjPRotUFQJ2ss5SfYJ2kvaQUAKTTD6R5ddBbwYEqxAqTYZGUmAG9kx+uAuyU9AOSB5vYAx3RQfxB8ImLCCIJDpz8rf5gdf0j6bH0KeMfMpg9ynvdJQe0AMLNFkr5CCii3SdJ0S5FkR3vfIOgK8UgqCNqzj5QS85CwlLNgl6TL4KO8y9NadN0KnFgcSJpiZhvMbCnwJgfCU58MtMw3HQRNEBNGELTB7+rX+Qb0skM8zeXAQklFJNNWKUXXAjN04LnVMkm9vsG+lhQFFVKO9z8eoo4gGDLxtdogGAZI+g2w0sxWt2kfBTwLzLaUSjgIGidWGEEwPLgZGDNA+0RgcUwWQTeJFUYQBEFQiVhhBEEQBJWICSMIgiCoREwYQRAEQSViwgiCIAgqERNGEARBUIn/A6NIeOwdrzxtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Data for plotting\n",
    "t = np.arange(0.0, 2.0, 0.01)\n",
    "s = 1 + np.sin(2 * np.pi * t)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "# fig = plt.figure(figsize=(15,10))\n",
    "ax.plot(t, s)\n",
    "\n",
    "ax.set(xlabel='time (s)', ylabel='voltage (mV)',\n",
    "       title='About as simple as it gets, folks')\n",
    "ax.grid()\n",
    "\n",
    "# fig.savefig(\"test.png\") # 保存绘制数据图\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Multiple subplots in one figure\n",
    "===============================\n",
    "\n",
    "Multiple axes (i.e. subplots) are created with the\n",
    ":func:`~matplotlib.pyplot.subplot` function:\n",
    "\n",
    ".. figure:: ../../gallery/subplots_axes_and_figures/images/sphx_glr_subplot_001.png\n",
    "   :target: ../../gallery/subplots_axes_and_figures/subplot.html\n",
    "   :align: center\n",
    "   :scale: 50\n",
    "\n",
    "   Subplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "x1 = np.linspace(0.0, 5.0)\n",
    "x2 = np.linspace(0.0, 2.0)\n",
    "\n",
    "y1 = np.cos(2 * np.pi * x1) * np.exp(-x1)\n",
    "y2 = np.cos(2 * np.pi * x2)\n",
    "\n",
    "plt.subplot(2, 1, 1) # 2行1列第1个\n",
    "plt.plot(x1, y1, 'o-')\n",
    "plt.title('A tale of 2 subplots')\n",
    "plt.ylabel('Damped oscillation')\n",
    "\n",
    "plt.subplot(2, 1, 2) # 2行1列第2个\n",
    "plt.plot(x2, y2, '.-')\n",
    "plt.xlabel('time (s)')\n",
    "plt.ylabel('Undamped')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Images\n",
    "======\n",
    "\n",
    "Matplotlib can display images (assuming equally spaced\n",
    "horizontal dimensions) using the :func:`~matplotlib.pyplot.imshow` function.\n",
    "\n",
    ".. figure:: ../../gallery/images_contours_and_fields/images/sphx_glr_image_demo_003.png\n",
    "   :target: ../../gallery/images_contours_and_fields/image_demo.html\n",
    "   :align: center\n",
    "   :scale: 50\n",
    "\n",
    "   Example of using :func:`~matplotlib.pyplot.imshow` to display a CT scan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://matplotlib.org/gallery/images_contours_and_fields/image_demo.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Histograms\n",
    "==========\n",
    "\n",
    "The :func:`~matplotlib.pyplot.hist` function automatically generates\n",
    "histograms and returns the bin counts or probabilities:\n",
    "\n",
    ".. figure:: ../../gallery/statistics/images/sphx_glr_histogram_features_001.png\n",
    "   :target: ../../gallery/statistics/histogram_features.html\n",
    "   :align: center\n",
    "   :scale: 50\n",
    "\n",
    "   Histogram Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "np.random.seed(19680801)\n",
    "\n",
    "# example data\n",
    "mu = 100  # mean of distribution\n",
    "sigma = 15  # standard deviation of distribution\n",
    "x = mu + sigma * np.random.randn(437)\n",
    "\n",
    "num_bins = 50\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# the histogram of the data\n",
    "n, bins, patches = ax.hist(x, num_bins, density=1)\n",
    "\n",
    "# add a 'best fit' line\n",
    "y = ((1 / (np.sqrt(2 * np.pi) * sigma)) *\n",
    "     np.exp(-0.5 * (1 / sigma * (bins - mu))**2))\n",
    "ax.plot(bins, y, '--')\n",
    "ax.set_xlabel('Smarts')\n",
    "ax.set_ylabel('Probability density')\n",
    "ax.set_title(r'Histogram of IQ: $\\mu=100$, $\\sigma=15$')\n",
    "\n",
    "# Tweak spacing to prevent clipping of ylabel\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(19)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Paths\n",
    "=====\n",
    "\n",
    "You can add arbitrary paths in Matplotlib using the\n",
    ":mod:`matplotlib.path` module:\n",
    "\n",
    ".. figure:: ../../gallery/shapes_and_collections/images/sphx_glr_path_patch_001.png\n",
    "   :target: ../../gallery/shapes_and_collections/path_patch.html\n",
    "   :align: center\n",
    "   :scale: 50\n",
    "\n",
    "   Path Patch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.path as mpath\n",
    "import matplotlib.patches as mpatches\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "Path = mpath.Path\n",
    "path_data = [\n",
    "    (Path.MOVETO, (1.58, -2.57)),\n",
    "    (Path.CURVE4, (0.35, -1.1)),\n",
    "    (Path.CURVE4, (-1.75, 2.0)),\n",
    "    (Path.CURVE4, (0.375, 2.0)),\n",
    "    (Path.LINETO, (0.85, 1.15)),\n",
    "    (Path.CURVE4, (2.2, 3.2)),\n",
    "    (Path.CURVE4, (3, 0.05)),\n",
    "    (Path.CURVE4, (2.0, -0.5)),\n",
    "    (Path.CLOSEPOLY, (1.58, -2.57)),\n",
    "    ]\n",
    "codes, verts = zip(*path_data)\n",
    "path = mpath.Path(verts, codes)\n",
    "patch = mpatches.PathPatch(path, facecolor='r', alpha=0.5)\n",
    "ax.add_patch(patch)\n",
    "\n",
    "# plot control points and connecting lines\n",
    "x, y = zip(*path.vertices)\n",
    "line, = ax.plot(x, y, 'go-')\n",
    "\n",
    "ax.grid()\n",
    "ax.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Three-dimensional plotting\n",
    "==========================\n",
    "\n",
    "The mplot3d toolkit (see `toolkit_mplot3d-tutorial` and\n",
    "`mplot3d-examples-index`) has support for simple 3d graphs\n",
    "including surface, wireframe, scatter, and bar charts.\n",
    "\n",
    ".. figure:: ../../gallery/mplot3d/images/sphx_glr_surface3d_001.png\n",
    "   :target: ../../gallery/mplot3d/surface3d.html\n",
    "   :align: center\n",
    "   :scale: 50\n",
    "\n",
    "   Surface3d\n",
    "\n",
    "Thanks to John Porter, Jonathon Taylor, Reinier Heeres, and Ben Root for\n",
    "the `mplot3d` toolkit. This toolkit is included with all standard Matplotlib\n",
    "installs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This import registers the 3D projection, but is otherwise unused.\n",
    "from mpl_toolkits.mplot3d import Axes3D  # noqa: F401 unused import\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "from matplotlib.ticker import LinearLocator, FormatStrFormatter\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.gca(projection='3d')\n",
    "\n",
    "# Make data.\n",
    "X = np.arange(-5, 5, 0.25)\n",
    "Y = np.arange(-5, 5, 0.25)\n",
    "X, Y = np.meshgrid(X, Y)\n",
    "R = np.sqrt(X**2 + Y**2)\n",
    "Z = np.sin(R)\n",
    "\n",
    "# Plot the surface.\n",
    "surf = ax.plot_surface(X, Y, Z, cmap=cm.coolwarm,\n",
    "                       linewidth=0, antialiased=False)\n",
    "\n",
    "# Customize the z axis.\n",
    "ax.set_zlim(-1.01, 1.01)\n",
    "ax.zaxis.set_major_locator(LinearLocator(10))\n",
    "ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))\n",
    "\n",
    "# Add a color bar which maps values to colors.\n",
    "fig.colorbar(surf, shrink=0.5, aspect=5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Streamplot\n",
    "==========\n",
    "\n",
    "The :meth:`~matplotlib.pyplot.streamplot` function plots the streamlines of\n",
    "a vector field. In addition to simply plotting the streamlines, it allows you\n",
    "to map the colors and/or line widths of streamlines to a separate parameter,\n",
    "such as the speed or local intensity of the vector field.\n",
    "\n",
    ".. figure:: ../../gallery/images_contours_and_fields/images/sphx_glr_plot_streamplot_001.png\n",
    "   :target: ../../gallery/images_contours_and_fields/plot_streamplot.html\n",
    "   :align: center\n",
    "   :scale: 50\n",
    "\n",
    "   Streamplot with various plotting options.\n",
    "\n",
    "This feature complements the :meth:`~matplotlib.pyplot.quiver` function for\n",
    "plotting vector fields. Thanks to Tom Flannaghan and Tony Yu for adding the\n",
    "streamplot function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x648 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "\n",
    "w = 3\n",
    "Y, X = np.mgrid[-w:w:100j, -w:w:100j]\n",
    "U = -1 - X**2 + Y\n",
    "V = 1 + X - Y**2\n",
    "speed = np.sqrt(U**2 + V**2)\n",
    "\n",
    "fig = plt.figure(figsize=(7, 9))\n",
    "gs = gridspec.GridSpec(nrows=3, ncols=2, height_ratios=[1, 1, 2])\n",
    "\n",
    "#  Varying density along a streamline\n",
    "ax0 = fig.add_subplot(gs[0, 0])\n",
    "ax0.streamplot(X, Y, U, V, density=[0.5, 1])\n",
    "ax0.set_title('Varying Density')\n",
    "\n",
    "# Varying color along a streamline\n",
    "ax1 = fig.add_subplot(gs[0, 1])\n",
    "strm = ax1.streamplot(X, Y, U, V, color=U, linewidth=2, cmap='autumn')\n",
    "fig.colorbar(strm.lines)\n",
    "ax1.set_title('Varying Color')\n",
    "\n",
    "#  Varying line width along a streamline\n",
    "ax2 = fig.add_subplot(gs[1, 0])\n",
    "lw = 5*speed / speed.max()\n",
    "ax2.streamplot(X, Y, U, V, density=0.6, color='k', linewidth=lw)\n",
    "ax2.set_title('Varying Line Width')\n",
    "\n",
    "# Controlling the starting points of the streamlines\n",
    "seed_points = np.array([[-2, -1, 0, 1, 2, -1], [-2, -1,  0, 1, 2, 2]])\n",
    "\n",
    "ax3 = fig.add_subplot(gs[1, 1])\n",
    "strm = ax3.streamplot(X, Y, U, V, color=U, linewidth=2,\n",
    "                     cmap='autumn', start_points=seed_points.T)\n",
    "fig.colorbar(strm.lines)\n",
    "ax3.set_title('Controlling Starting Points')\n",
    "\n",
    "# Displaying the starting points with blue symbols.\n",
    "ax3.plot(seed_points[0], seed_points[1], 'bo')\n",
    "ax3.set(xlim=(-w, w), ylim=(-w, w))\n",
    "\n",
    "# Create a mask\n",
    "mask = np.zeros(U.shape, dtype=bool)\n",
    "mask[40:60, 40:60] = True\n",
    "U[:20, :20] = np.nan\n",
    "U = np.ma.array(U, mask=mask)\n",
    "\n",
    "ax4 = fig.add_subplot(gs[2:, :])\n",
    "ax4.streamplot(X, Y, U, V, color='r')\n",
    "ax4.set_title('Streamplot with Masking')\n",
    "\n",
    "ax4.imshow(~mask, extent=(-w, w, -w, w), alpha=0.5,\n",
    "          interpolation='nearest', cmap='gray', aspect='auto')\n",
    "ax4.set_aspect('equal')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ellipses\n",
    "========\n",
    "\n",
    "In support of the `Phoenix <http://www.jpl.nasa.gov/news/phoenix/main.php>`_\n",
    "mission to Mars (which used Matplotlib to display ground tracking of\n",
    "spacecraft), Michael Droettboom built on work by Charlie Moad to provide\n",
    "an extremely accurate 8-spline approximation to elliptical arcs (see\n",
    ":class:`~matplotlib.patches.Arc`), which are insensitive to zoom level.\n",
    "\n",
    ".. figure:: ../../gallery/shapes_and_collections/images/sphx_glr_ellipse_demo_001.png\n",
    "   :target: ../../gallery/shapes_and_collections/ellipse_demo.html\n",
    "   :align: center\n",
    "   :scale: 50\n",
    "\n",
    "   Ellipse Demo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from matplotlib.patches import Ellipse\n",
    "\n",
    "NUM = 250\n",
    "\n",
    "ells = [Ellipse(xy=np.random.rand(2) * 10,\n",
    "                width=np.random.rand(), height=np.random.rand(),\n",
    "                angle=np.random.rand() * 360)\n",
    "        for i in range(NUM)]\n",
    "\n",
    "fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'})\n",
    "for e in ells:\n",
    "    ax.add_artist(e)\n",
    "    e.set_clip_box(ax.bbox)\n",
    "    e.set_alpha(np.random.rand())\n",
    "    e.set_facecolor(np.random.rand(3))\n",
    "\n",
    "ax.set_xlim(0, 10)\n",
    "ax.set_ylim(0, 10)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bar charts\n",
    "==========\n",
    "\n",
    "Use the :func:`~matplotlib.pyplot.bar` function to make bar charts, which\n",
    "includes customizations such as error bars:\n",
    "\n",
    ".. figure:: ../../gallery/statistics/images/sphx_glr_barchart_demo_001.png\n",
    "   :target: ../../gallery/statistics/barchart_demo.html\n",
    "   :align: center\n",
    "   :scale: 50\n",
    "\n",
    "   Barchart Demo\n",
    "\n",
    "You can also create stacked bars\n",
    "(`bar_stacked.py <../../gallery/lines_bars_and_markers/bar_stacked.html>`_),\n",
    "or horizontal bar charts\n",
    "(`barh.py <../../gallery/lines_bars_and_markers/barh.html>`_)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "from collections import namedtuple\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "Student = namedtuple('Student', ['name', 'grade', 'gender'])\n",
    "Score = namedtuple('Score', ['score', 'percentile'])\n",
    "\n",
    "# GLOBAL CONSTANTS\n",
    "testNames = ['Pacer Test', 'Flexed Arm\\n Hang', 'Mile Run', 'Agility',\n",
    "             'Push Ups']\n",
    "testMeta = dict(zip(testNames, ['laps', 'sec', 'min:sec', 'sec', '']))\n",
    "\n",
    "\n",
    "def attach_ordinal(num):\n",
    "    \"\"\"helper function to add ordinal string to integers\n",
    "\n",
    "    1 -> 1st\n",
    "    56 -> 56th\n",
    "    \"\"\"\n",
    "    suffixes = {str(i): v\n",
    "                for i, v in enumerate(['th', 'st', 'nd', 'rd', 'th',\n",
    "                                       'th', 'th', 'th', 'th', 'th'])}\n",
    "\n",
    "    v = str(num)\n",
    "    # special case early teens\n",
    "    if v in {'11', '12', '13'}:\n",
    "        return v + 'th'\n",
    "    return v + suffixes[v[-1]]\n",
    "\n",
    "\n",
    "def format_score(scr, test):\n",
    "    \"\"\"\n",
    "    Build up the score labels for the right Y-axis by first\n",
    "    appending a carriage return to each string and then tacking on\n",
    "    the appropriate meta information (i.e., 'laps' vs 'seconds'). We\n",
    "    want the labels centered on the ticks, so if there is no meta\n",
    "    info (like for pushups) then don't add the carriage return to\n",
    "    the string\n",
    "    \"\"\"\n",
    "    md = testMeta[test]\n",
    "    if md:\n",
    "        return '{0}\\n{1}'.format(scr, md)\n",
    "    else:\n",
    "        return scr\n",
    "\n",
    "\n",
    "def format_ycursor(y):\n",
    "    y = int(y)\n",
    "    if y < 0 or y >= len(testNames):\n",
    "        return ''\n",
    "    else:\n",
    "        return testNames[y]\n",
    "\n",
    "\n",
    "def plot_student_results(student, scores, cohort_size):\n",
    "    #  create the figure\n",
    "    fig, ax1 = plt.subplots(figsize=(9, 7))\n",
    "    fig.subplots_adjust(left=0.115, right=0.88)\n",
    "    fig.canvas.set_window_title('Eldorado K-8 Fitness Chart')\n",
    "\n",
    "    pos = np.arange(len(testNames))\n",
    "\n",
    "    rects = ax1.barh(pos, [scores[k].percentile for k in testNames],\n",
    "                     align='center',\n",
    "                     height=0.5,\n",
    "                     tick_label=testNames)\n",
    "\n",
    "    ax1.set_title(student.name)\n",
    "\n",
    "    ax1.set_xlim([0, 100])\n",
    "    ax1.xaxis.set_major_locator(MaxNLocator(11))\n",
    "    ax1.xaxis.grid(True, linestyle='--', which='major',\n",
    "                   color='grey', alpha=.25)\n",
    "\n",
    "    # Plot a solid vertical gridline to highlight the median position\n",
    "    ax1.axvline(50, color='grey', alpha=0.25)\n",
    "\n",
    "    # Set the right-hand Y-axis ticks and labels\n",
    "    ax2 = ax1.twinx()\n",
    "\n",
    "    scoreLabels = [format_score(scores[k].score, k) for k in testNames]\n",
    "\n",
    "    # set the tick locations\n",
    "    ax2.set_yticks(pos)\n",
    "    # make sure that the limits are set equally on both yaxis so the\n",
    "    # ticks line up\n",
    "    ax2.set_ylim(ax1.get_ylim())\n",
    "\n",
    "    # set the tick labels\n",
    "    ax2.set_yticklabels(scoreLabels)\n",
    "\n",
    "    ax2.set_ylabel('Test Scores')\n",
    "\n",
    "    xlabel = ('Percentile Ranking Across {grade} Grade {gender}s\\n'\n",
    "              'Cohort Size: {cohort_size}')\n",
    "    ax1.set_xlabel(xlabel.format(grade=attach_ordinal(student.grade),\n",
    "                                 gender=student.gender.title(),\n",
    "                                 cohort_size=cohort_size))\n",
    "\n",
    "    rect_labels = []\n",
    "    # Lastly, write in the ranking inside each bar to aid in interpretation\n",
    "    for rect in rects:\n",
    "        # Rectangle widths are already integer-valued but are floating\n",
    "        # type, so it helps to remove the trailing decimal point and 0 by\n",
    "        # converting width to int type\n",
    "        width = int(rect.get_width())\n",
    "\n",
    "        rankStr = attach_ordinal(width)\n",
    "        # The bars aren't wide enough to print the ranking inside\n",
    "        if width < 40:\n",
    "            # Shift the text to the right side of the right edge\n",
    "            xloc = 5\n",
    "            # Black against white background\n",
    "            clr = 'black'\n",
    "            align = 'left'\n",
    "        else:\n",
    "            # Shift the text to the left side of the right edge\n",
    "            xloc = -5\n",
    "            # White on magenta\n",
    "            clr = 'white'\n",
    "            align = 'right'\n",
    "\n",
    "        # Center the text vertically in the bar\n",
    "        yloc = rect.get_y() + rect.get_height() / 2\n",
    "        label = ax1.annotate(rankStr, xy=(width, yloc), xytext=(xloc, 0),\n",
    "                            textcoords=\"offset points\",\n",
    "                            ha=align, va='center',\n",
    "                            color=clr, weight='bold', clip_on=True)\n",
    "        rect_labels.append(label)\n",
    "\n",
    "    # make the interactive mouse over give the bar title\n",
    "    ax2.fmt_ydata = format_ycursor\n",
    "    # return all of the artists created\n",
    "    return {'fig': fig,\n",
    "            'ax': ax1,\n",
    "            'ax_right': ax2,\n",
    "            'bars': rects,\n",
    "            'perc_labels': rect_labels}\n",
    "\n",
    "\n",
    "student = Student('Johnny Doe', 2, 'boy')\n",
    "scores = dict(zip(testNames,\n",
    "                  (Score(v, p) for v, p in\n",
    "                   zip(['7', '48', '12:52', '17', '14'],\n",
    "                       np.round(np.random.uniform(0, 1,\n",
    "                                                  len(testNames)) * 100, 0)))))\n",
    "cohort_size = 62  # The number of other 2nd grade boys\n",
    "\n",
    "arts = plot_student_results(student, scores, cohort_size)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pie charts\n",
    "==========\n",
    "\n",
    "The :func:`~matplotlib.pyplot.pie` function allows you to create pie\n",
    "charts.  Optional features include auto-labeling the percentage of area,\n",
    "exploding one or more wedges from the center of the pie, and a shadow effect.\n",
    "Take a close look at the attached code, which generates this figure in just\n",
    "a few lines of code.\n",
    "\n",
    ".. figure:: ../../gallery/pie_and_polar_charts/images/sphx_glr_pie_features_001.png\n",
    "   :target: ../../gallery/pie_and_polar_charts/pie_features.html\n",
    "   :align: center\n",
    "   :scale: 50\n",
    "\n",
    "   Pie Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Pie chart, where the slices will be ordered and plotted counter-clockwise:\n",
    "labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'\n",
    "sizes = [15, 30, 45, 10]\n",
    "explode = (0, 0.1, 0, 0)  # only \"explode\" the 2nd slice (i.e. 'Hogs')\n",
    "\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=90)\n",
    "ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tables\n",
    "======\n",
    "\n",
    "The :func:`~matplotlib.pyplot.table` function adds a text table\n",
    "to an axes.\n",
    "\n",
    ".. figure:: ../../gallery/misc/images/sphx_glr_table_demo_001.png\n",
    "   :target: ../../gallery/misc/table_demo.html\n",
    "   :align: center\n",
    "   :scale: 50\n",
    "\n",
    "   Table Demo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "data = [[ 66386, 174296,  75131, 577908,  32015],\n",
    "        [ 58230, 381139,  78045,  99308, 160454],\n",
    "        [ 89135,  80552, 152558, 497981, 603535],\n",
    "        [ 78415,  81858, 150656, 193263,  69638],\n",
    "        [139361, 331509, 343164, 781380,  52269]]\n",
    "\n",
    "columns = ('Freeze', 'Wind', 'Flood', 'Quake', 'Hail')\n",
    "rows = ['%d year' % x for x in (100, 50, 20, 10, 5)]\n",
    "\n",
    "values = np.arange(0, 2500, 500)\n",
    "value_increment = 1000\n",
    "\n",
    "# Get some pastel shades for the colors\n",
    "colors = plt.cm.BuPu(np.linspace(0, 0.5, len(rows)))\n",
    "n_rows = len(data)\n",
    "\n",
    "index = np.arange(len(columns)) + 0.3\n",
    "bar_width = 0.4\n",
    "\n",
    "# Initialize the vertical-offset for the stacked bar chart.\n",
    "y_offset = np.zeros(len(columns))\n",
    "\n",
    "# Plot bars and create text labels for the table\n",
    "cell_text = []\n",
    "for row in range(n_rows):\n",
    "    plt.bar(index, data[row], bar_width, bottom=y_offset, color=colors[row])\n",
    "    y_offset = y_offset + data[row]\n",
    "    cell_text.append(['%1.1f' % (x / 1000.0) for x in y_offset])\n",
    "# Reverse colors and text labels to display the last value at the top.\n",
    "colors = colors[::-1]\n",
    "cell_text.reverse()\n",
    "\n",
    "# Add a table at the bottom of the axes\n",
    "the_table = plt.table(cellText=cell_text,\n",
    "                      rowLabels=rows,\n",
    "                      rowColours=colors,\n",
    "                      colLabels=columns,\n",
    "                      loc='bottom')\n",
    "\n",
    "# Adjust layout to make room for the table:\n",
    "plt.subplots_adjust(left=0.2, bottom=0.2)\n",
    "\n",
    "plt.ylabel(\"Loss in ${0}'s\".format(value_increment))\n",
    "plt.yticks(values * value_increment, ['%d' % val for val in values])\n",
    "plt.xticks([])\n",
    "plt.title('Loss by Disaster')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scatter plots\n",
    "=============\n",
    "\n",
    "The :func:`~matplotlib.pyplot.scatter` function makes a scatter plot\n",
    "with (optional) size and color arguments. This example plots changes\n",
    "in Google's stock price, with marker sizes reflecting the\n",
    "trading volume and colors varying with time. Here, the\n",
    "alpha attribute is used to make semitransparent circle markers.\n",
    "\n",
    ".. figure:: ../../gallery/lines_bars_and_markers/images/sphx_glr_scatter_demo2_001.png\n",
    "   :target: ../../gallery/lines_bars_and_markers/scatter_demo2.html\n",
    "   :align: center\n",
    "   :scale: 50\n",
    "\n",
    "   Scatter Demo2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'D:\\\\pcApp\\\\Anaconda3\\\\lib\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\sample_data\\\\goog.npz'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-23-fac6b611c6d6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;31m# volume, adj_close from the mpl-data/example directory. The record array\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;31m# stores the date as an np.datetime64 with a day unit ('D') in the date column.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m \u001b[1;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_sample_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'goog.npz'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mdatafile\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m     \u001b[0mprice_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdatafile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'price_data'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mview\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrecarray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[0mprice_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mprice_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m250\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m  \u001b[1;31m# get the most recent 250 trading days\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\pcApp\\Anaconda3\\lib\\site-packages\\matplotlib\\cbook\\__init__.py\u001b[0m in \u001b[0;36mget_sample_data\u001b[1;34m(fname, asfileobj)\u001b[0m\n\u001b[0;32m    680\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mgzip\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    681\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 682\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    683\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    684\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'D:\\\\pcApp\\\\Anaconda3\\\\lib\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\sample_data\\\\goog.npz'"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cbook as cbook\n",
    "\n",
    "# Load a numpy record array from yahoo csv data with fields date, open, close,\n",
    "# volume, adj_close from the mpl-data/example directory. The record array\n",
    "# stores the date as an np.datetime64 with a day unit ('D') in the date column.\n",
    "with cbook.get_sample_data('goog.npz') as datafile:\n",
    "    price_data = np.load(datafile)['price_data'].view(np.recarray)\n",
    "price_data = price_data[-250:]  # get the most recent 250 trading days\n",
    "\n",
    "delta1 = np.diff(price_data.adj_close) / price_data.adj_close[:-1]\n",
    "\n",
    "# Marker size in units of points^2\n",
    "volume = (15 * price_data.volume[:-2] / price_data.volume[0])**2\n",
    "close = 0.003 * price_data.close[:-2] / 0.003 * price_data.open[:-2]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(delta1[:-1], delta1[1:], c=close, s=volume, alpha=0.5)\n",
    "\n",
    "ax.set_xlabel(r'$\\Delta_i$', fontsize=15)\n",
    "ax.set_ylabel(r'$\\Delta_{i+1}$', fontsize=15)\n",
    "ax.set_title('Volume and percent change')\n",
    "\n",
    "ax.grid(True)\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GUI widgets\n",
    "===========\n",
    "\n",
    "Matplotlib has basic GUI widgets that are independent of the graphical\n",
    "user interface you are using, allowing you to write cross GUI figures\n",
    "and widgets.  See :mod:`matplotlib.widgets` and the\n",
    "`widget examples <../../gallery/index.html>`_.\n",
    "\n",
    ".. figure:: ../../gallery/widgets/images/sphx_glr_slider_demo_001.png\n",
    "   :target: ../../gallery/widgets/slider_demo.html\n",
    "   :align: center\n",
    "   :scale: 50\n",
    "\n",
    "   Slider and radio-button GUI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.widgets import Slider, Button, RadioButtons\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.subplots_adjust(left=0.25, bottom=0.25)\n",
    "t = np.arange(0.0, 1.0, 0.001)\n",
    "a0 = 5\n",
    "f0 = 3\n",
    "delta_f = 5.0\n",
    "s = a0 * np.sin(2 * np.pi * f0 * t)\n",
    "l, = plt.plot(t, s, lw=2)\n",
    "ax.margins(x=0)\n",
    "\n",
    "axcolor = 'lightgoldenrodyellow'\n",
    "axfreq = plt.axes([0.25, 0.1, 0.65, 0.03], facecolor=axcolor)\n",
    "axamp = plt.axes([0.25, 0.15, 0.65, 0.03], facecolor=axcolor)\n",
    "\n",
    "sfreq = Slider(axfreq, 'Freq', 0.1, 30.0, valinit=f0, valstep=delta_f)\n",
    "samp = Slider(axamp, 'Amp', 0.1, 10.0, valinit=a0)\n",
    "\n",
    "\n",
    "def update(val):\n",
    "    amp = samp.val\n",
    "    freq = sfreq.val\n",
    "    l.set_ydata(amp*np.sin(2*np.pi*freq*t))\n",
    "    fig.canvas.draw_idle()\n",
    "\n",
    "\n",
    "sfreq.on_changed(update)\n",
    "samp.on_changed(update)\n",
    "\n",
    "resetax = plt.axes([0.8, 0.025, 0.1, 0.04])\n",
    "button = Button(resetax, 'Reset', color=axcolor, hovercolor='0.975')\n",
    "\n",
    "\n",
    "def reset(event):\n",
    "    sfreq.reset()\n",
    "    samp.reset()\n",
    "button.on_clicked(reset)\n",
    "\n",
    "rax = plt.axes([0.025, 0.5, 0.15, 0.15], facecolor=axcolor)\n",
    "radio = RadioButtons(rax, ('red', 'blue', 'green'), active=0)\n",
    "\n",
    "\n",
    "def colorfunc(label):\n",
    "    l.set_color(label)\n",
    "    fig.canvas.draw_idle()\n",
    "radio.on_clicked(colorfunc)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filled curves\n",
    "=============\n",
    "\n",
    "The :func:`~matplotlib.pyplot.fill` function lets you\n",
    "plot filled curves and polygons:\n",
    "\n",
    ".. figure:: ../../gallery/lines_bars_and_markers/images/sphx_glr_fill_001.png\n",
    "   :target: ../../gallery/lines_bars_and_markers/fill.html\n",
    "   :align: center\n",
    "   :scale: 50\n",
    "\n",
    "   Fill\n",
    "\n",
    "Thanks to Andrew Straw for adding this function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "def koch_snowflake(order, scale=10):\n",
    "    \"\"\"\n",
    "    Return two lists x, y of point coordinates of the Koch snowflake.\n",
    "\n",
    "    Arguments\n",
    "    ---------\n",
    "    order : int\n",
    "        The recursion depth.\n",
    "    scale : float\n",
    "        The extent of the snowflake (edge length of the base triangle).\n",
    "    \"\"\"\n",
    "    def _koch_snowflake_complex(order):\n",
    "        if order == 0:\n",
    "            # initial triangle\n",
    "            angles = np.array([0, 120, 240]) + 90\n",
    "            return scale / np.sqrt(3) * np.exp(np.deg2rad(angles) * 1j)\n",
    "        else:\n",
    "            ZR = 0.5 - 0.5j * np.sqrt(3) / 3\n",
    "\n",
    "            p1 = _koch_snowflake_complex(order - 1)  # start points\n",
    "            p2 = np.roll(p1, shift=-1)  # end points\n",
    "            dp = p2 - p1  # connection vectors\n",
    "\n",
    "            new_points = np.empty(len(p1) * 4, dtype=np.complex128)\n",
    "            new_points[::4] = p1\n",
    "            new_points[1::4] = p1 + dp / 3\n",
    "            new_points[2::4] = p1 + dp * ZR\n",
    "            new_points[3::4] = p1 + dp / 3 * 2\n",
    "            return new_points\n",
    "\n",
    "    points = _koch_snowflake_complex(order)\n",
    "    x, y = points.real, points.imag\n",
    "    return x, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ymYDeAV8jzzNaHShvdc0rv2JRCmQyASarHeV9c80B4HB9FwAgOULLpEwBQxBFzwx0EQRBDiAPQJ0oihcOt29ubq6Yl5fnkfMSeYvV7kSHyYqfvLEXr99yEtQK2aClBZ1OEVaHE//3VRFe2VYpXaABKCsuFB/ftXzIa/74l0VYmhmFldPjoFHynTL5PkEQ9oqimDuafT3ZYr4XQKEHj0ckKZVChvhwDd768VLo1Irj1vuVyQRolHKsYEvN45ZkRA17zX9x3gycOyeRSZkCkkfeMQuCkALgAgB/AnC/J45J5CuGuvkbLXY8v6kUXx5qnOSIAt97eTUI0yhw0bwkzEnWH7edCZkCmadazM8A+DmAISszCIJwmyAIeYIg5LW0tHjotETS0akVKG7sdr8HJc+xOUS8ubMambE6qUMhmnQTTsyCIFwIoFkUxb3D7SeK4ouiKOaKopgbGxs70dMSSc5osaOi1QiFTIBGKUOUTiV1SH4vTKNAWN9ymwq5gEO1hhG+gyjweKIrezmAiwVBOB+ABkC4IAhviKJ4vQeOTeSzdGoF1j+wEs+tL8HijCgszYzGnW/uxRfs2h6XmFAVNj64EjJBwP3vHsC/rl8EQRCkDoto0k04MYui+AiARwBAEISVAB5kUqZgctfpWZDJXAkkI4Zdr+MVG6aBTqWATCbgheuYlCl4cR4z0QT1J2UAmJOkxxWLUiSMxj/NTAzHbadmoMvsWohi4DUlCjYeTcyiKG4aaQ4z+Z9eq8N9w6ThnTc3Eb+9aBaUciaWsbjr9KlYnZOCiBC+px+Npi6z1CGQF7EkJw1JFEUU1HXhw/21MNucuHZJKmYlhh83t5SOKagzYH1hM1eoGqN3dtcgLSoEU2NDoVPztjSU0uYe5Nd24pVtlfjT6jlIj9JBH6KUOizyMN5haUiCIODZDSV4ZVsl3t5djb9+XcykPII3d1Xhb+uOSh2G39la2oq73toHBXsahvXd0Rbc/+5BHKoz4Iev7AF4uQIS77J0nJe2VqDB0AsAUA1IxIq+936tPRa8sKlMkth80bt5NShu7AYAKPngMm4KmQwCBNgdTvzlSxYR7JdX2Y6vChoAAErFsd8vQRAg7/ub/NPnRySJjbzDY7Wyx4K1sn1Ta48Fd7+1DwdqOhEXpkFcmBp5VR2D9slJi0CnyYa6zl4sSI3AM1cvQFKEVqKIpWW02HHHG3txoLoTYRoFkiO12FfdCYeT3djjNSMhDIIgoLChCwvTIvDI+TOxeEqU1GFJ5mf/O4Btpa2wOZzIigtFUUM3ui129/asuFCEqOTIrzVgXooePz0jG6tmxUsYMQ1lLLWy+TKH3GJC1XA6XQvOV7ebUN1uOm6f/dWd7n+brPagTcqAax5zqFqBbosd3RY76g0ckDNRRX09DwBwtKkH81MiJIxGeqlRIWjutgAA9lR2HLe9tPnY6maH67uQOyVy0mIj72G/G7m19lgQHapyV14aTohKjpSIENR39k5CZL7JaLFDLhMQzYpfXrE0MxqH6jpH3jGAtRstSI8OGdW+K7JisL8muK9XoGBXdhASRXHY4g0vba3AY58N/87qgbOm4Z4zs8d9Dn8y0mf5aH8d7vvfgUmMKPBdND8Jz12bM+T2YPr9OlxvwIXPbcVwt+rlWdF489al4z4HeZ9Uyz6SH+gwWvHz9/OH3adhFK3ghhHmUf7qowI0B8BcS7PNgZ++M3zSrTcEb6+BtzSOcE1f2FyGvVXHd+36o3vfOQCT1T7k9oZO87BJGQBaui2wO4ZcQwj/21ODdUeaxhsiTTK2mIPIi9+V4V+by9FutCI5QouXbs7FjIRw9/bWHguu+tcOVLWbRhzAJBNc77/e/vHSQe+ZK1qNuPHlXajt6EWEVombT87AvauGbln7snfzavDEV8Vo7bEgIVyDv129AMumRru3Gy12XPGvHShr7oF1mJsijU+iXoNnrl6AkzIHX/PLX9iO8hYj1AoZzpwZh2euGbpl7cvyKttxz9v70WAwIyZUhfvPmo4fnJQ2aJ8fvrIbuyraYbI6RjxeXJgaf7hkDs6dk+D+ms3hxDUv7kRBnQFymYAlGVF45ebFbD1LgC1mcusy23C43rVCz6L0SLQbrQAAh1NEdlwYAGBHWRsA1+CviBDlqEYVO0VAq5S7k3L/MaZEh0AuCBBFoMNkw8J01+Cdo03daOuxePbDeYHF7nC3xJZMiUJrX8zdZhsWpLo+S/9n1akVSInUMil7SbfZjvnDXPNuix1z+waHGUw2FDZ0SRbrWPR/ljnJevT0jbBu7bFiSYZr4Nb+6g6Yba5EPCspfFRJGQDajFYs7hv8tbuiHU6nCKVchqmxOljsTpisDsxKDIcgCDDbHNhfHRg9DoGIo7ID2Lt7arC+qAk17b24bGEyPstvcG9r7DLjV2sPITNWh6e+OYrbT5sKm8OJfdWjHzxS1NiNP352BHqtEs9tKMV9Z2Wj0WBGZdux0dyPf1mE4sZufJbfgCidCmfOjMN1J6V79HN6yicH67GpqBn7qjtw/dJ0bCxudm8zWh14+IN8zEvR49n1JbgqNxXhWiXWFbJ70Ft6LHY8/EE+5ibr8ff1JbhmcSpC1UqsLzr2c/n35jKIoohdFe1oNJhxaU4yfrAkDVqVXMLIT2xLSQu2lrTizV3VuG9VNgrqDOg2H+vC/vVHBVg1Mx5v7a7G3GQ95ibr8d/tVaM+vsMp4uEP8rE0Mxr/2VKOVTPjkRoVgrX769z7vL6jClE6FQrqDMivM+AHS9Jw+cIURHIAo09hV3YA+8eGEjz5jW9VofrJyql4+NwZUodxQm/vrsYjHx6SOgyagFUz47DmpsVSh3FCe6vacfkLO6QOY5CctAisvXO51GEEBXZlEwD45OCYfT4YU7+8E8wTJf9ypL4LvaPs+p1svvj7Vd5iREff6y3yHWwxB7h1R5pw639941o/e20OLp6fJHUYw9pX3YHLX9g+4ihY8j13n56FB8+ZLnUYw2ruMuP0JzfB6AMPD9cuScNfLpsrdRhBgy1mcpuXoveJJQhlApCT6vtVnLLjQhHG1Y380qJ03696FRumRmrU6AqGeJs/XK9gxTtQAHthUxk2FDX5xBKEThG45+39OGNGHH46TGESKf13RyW+PdKELvPQc0rJdz366WFsL2vF/WdN98nBX18easCXBY2Dyo5K6cmvi1HY0IW7T8/i4C8fwxZzAJPLTlxfVyoHajrh9OE+Yp1KgS0lrVKHQeNU2WZCfafZJ5MyAKREhuCTg/VSh+HW2GVGQZ2BSdkHMTEHsPxaA0LVCqjkMiTpNVApJv/HPfDcISo5CuoMkx7DaB2oca0SJZcJSNJroFX65g2ejhEEIEmvgU4lh0ouQ2Wb0WcHf+2pbEdUXxJM1GtGVZPeG/rPrZAJaOmxcPCXD+LgrwC3p7Id+6o6cPtpU/H6jkr85uPDk3r+R86b4T53erQOp06LndTzj1VJUzc+3F+Hh8+dgc/zG3DXW/ukDomGcfnCFDx11XzsKGvD4XoDbj0lU+qQhmXoteHRTw/j6asWoKixCxc8u3VSlwldOT0Wr/5wCYoau/DZwQafHywXSLjsI7ktnhKF3L5BHon6yV+iMUGvAQBcvzTdL8oAZseH4ed9N6vECI3E0dBIEvt+v5ZNjcbSTN9ft1mvVeKpK+cDAKJ0KqgVslFX9vKE/us1IyEc0+PDJu28NDbsyg4C/QkxOlSF3144a9LO+/C5M5DcV7LTH5Jyv/5Y1QoZ/u/yuZD5T+hB5cZl6Vg0YP1hf/kd64+z1+rAk1fOh16rnJTzXpaTjNOnx6G/l9RfrlcwYmL2IQaTzV071xty0iLxoxUZmJMcPvLOE5QZq8NPVk5F7hTvtWLMNoe7lrU3zE7S4+rFaViR7dvd78FIpZDhF+fNwOnT47x6njovrjeeHq3D+XMTceWiFK+dY6CHz5uBs2cneDUhe/N6BRMmZh/gcIrYU9mOxz4/gr98UYg9le3wxrv/kqZufHygDofrvV/sv7zFiHfzalDU6J1z5VW2469fF+P3nxzG7op2WO2eX0iiotWIb480YUcZR2r7GqvdiX9tKvPaQgwFdQa8uq0Cd725D7sr2mHotXn8HI0GM3aWt+GjA5MzUvuFTWXI89K9pbixGx/srcUNL+3C7op2rz4wBwMmZh8glwl4YVMZ3t9bizd3VePVbZVeeaotbOzGve8cmLSqVj9/Px8Ha0a/KMZYvLOnBi9trcBn+Q146ptir4w4b+jsxY//m+cT88DpeM9uKB20MIsnbS9rxe8/PYIDNZ342f8OQO2F3y+bw4mbXt49aUns1e2VeGNnlVfuLQV1Bjzw3kGUtxhx2+t5UPD9z4QwMUvomXVH0dxlBuD6I+3Xv4xgTbsJz28qndA5CuoMeHOXa4UamxdalSOx9iW1D/bWIq+yfULHemlrBUqbewAMvl72vlGtHUYr/vp10YTOUdNuwgubygCAyzn6gf7fg/zaTryzu3pCx9pQ1IRvjzT1HffYw5jd6YRTFOFwivjNRwUTOofJasdjnx0B4Oopm+xJMf2fq7LViBe/K5vQsfZXd+DdvJq+4x77W3E4RfdI899+XDCpo84DBadLSaDB0Is73tiHI/UGxIVpoNcqceR7a8nOSQ5He48VrUYrZieF4z835iImVD2m8zz8fj42FDfDZLEjMzYUla1GdHvxHfaJ6FRy97nVShmWZ8Xg72Nc2N7Qa8Mtr+5BQb0Beq0ScWEaHPrefOgZCWEwWu2o7zRjdlI4nr0mB1NidGM6z5+/KMSnB+vRbrRiWnwYajtM6DB5vguTPEcuEzArMRwNBjPMNgfmJuvx8s2Lx1RkxOkUcfsbe7Gn78ExNTIEJc3dMNuOJZsp0SFQymUoae7BjIQw/PL8mWOe+vf6ziq8vLUCFa1GzEkOR1uPFQ0G85iO4QlzksPR2m1Fh8l1b3nppsVjLjJy/7sH8N3RVphtDmTE6FDRahw0PiYlUotQtQJFjd3IjgvFT8/MxkU+Xiff2zhdyscl6rWwO5ywOUTUdfaecMBEQd2xRK2QCWNOygCQHR+K//U90X4/kU0Wo9XhPne3BciKDR3zMfRaJULUCphtTphtFjR1Hd/1N7DMYY/ZPuakDACzEsPx4nflAKS7XjQ2Dqc46GcVE6Yec+UvmUxAelSIu7XcaTr+Zz9wjfHyVuO46kwvTIvAb1qNAAb/fU+2gefWKOXjqvyVHReGD/e51nk+0d9Kbcexe1pJcw+WZPj+VDZfwq5sCdR39iI7LhSho1gsQa9VIjlCO673UO1GK2Yk+M5cxey40HENojH02hCtUyF6FDcQjVKGuSl6VPbdAMei3tCL+X6w0AadWEK4BmqFbMyVv5xOERa7E1OiR7e4xFkz45FfO/YHt6NN3TglO2bM3+ctkSFKxIdrxlX5q9NkHfU86FUz43BoHNcrmLEr24ucThGyYQZBvLCpDP/31fDvRH9/0SzcvDxj3OeoaTfhjKc2ST6ASSYA3/zsNGTFDd1iHumzvLunBj//IH/Y89xzRhYeOHvoakYjnaPDaMWpT2yc9C5/mrh/Xb8Q585JHHL7SD/7AzWduPSf24Y9xzmz4/HvG4bujRzpHA6niHOe+c49VkJKf1o9B9edlD7k9pE+S0WrEaue3jzsO+TFUyLx3h0nD7k9mOZUc9lHH9Bg6B0xiVS0jvzHWTFCy+++/x0YtjVd02GSPCkDrtWlqtqG/iyGXhvufnv48pflo2gFj3S9fv1xwbBxNHaZYbL5Zq1lGl5Fq2nIbf3vkc3D/GxH8/dY3d4L5zCJaM3Wcmwqbh5ye7fZ5jNTiUbqVbrnnf3Dtqar200jDuyq7zQPe80/2FeHtfvrhg80CLHF7AV/+/YoXtpagR6LHbFharx7+zJkDHjn2WDoxep/bkdTt3nEUZkywfVO+uO7lw96z1zU2IXr/rMLbUYrQtUK3Hn6VNy5MmvQ9/7kjb3YWNw8aBCLlNQKGZZNjcarP1wy6Ouvba/Ek18Xo9tiR2SIEi/fvBg5acfe4Rl6bbj4H1tR29E7qhGesWFqvHf7skHvmes7e7H6+W1o7rYgRCnHdUvT8cvzZw76vl+uPYS1++rQy8TslxQyAZllHX91AAAgAElEQVSxOnx814pB75m3lLTgnrf3o9NkQ7hGgd9dNBuXDyjq4XSKuPrFHThYYxjVSPyIECWeuXoBVg4obmK2OXDpP7ehtLkHcpmAU7JjsOamxYO+b82Wcvx9XYnP9MbIBCA5UotP7lox6D1zQZ0BN768G+1GK8LUCty7Kvu4GuS3vrYHW0paYRnFTI9wjQKPXjIbq3MGX/Or/r3D/UpgXooe796+bNgWur9ji1kCLd0WHOkr3LEoPdI9QlGrlCO9b2H0zUdbALgSbUSIclRTJZyiK9H0J+X+Y0yNDYVC7vol7rHYsbivwtbBmk50mlxPuQtSI3wmKQOAxe7EvBTXO9weix17q1yjYHOnRLpvViKAmYmuymT9n1WvVSIlUjvqaRc6lRxp37vmSRFaRGhVEEXXgLSFaa44Spt7UNvhamnlpEYwKfsxu1PE1NhQaFVyiKKI7/p+9vNTI9ytti6z3T0QaUdZGyx2B2QyAbOT9KOeHmeyOtwPjltKWiCKIjRKOabGhcLe9766//e8y2zDvr4iKIunRPlMUgZc95bEcK07Kff/rUyLD3OXoe222N3V+/ZVd6DL7BojMj8lYlRJGXBd8/770/ayVljtzr5rHg6rwwmrw4nZSeGQyQRY7U7sKGvz5Mf0SxyV7QGvbqvA+qJmdJisOG9OIj4dsOZqdbsJv/gwH4l6Lf65sRS3n5YJQ69tTIulH6jpxKOfHoZKLsMr2ypx1+lZqGozDhqd/Oinh3HenER8cqAeSREa5E6JwktbKzz6OT3hlW0VUCtkOFjTidLmHly+KAXrCpvc2ztNNjz43kHMSAjDPzaW4gdL0qGUC9hWOvo/1so2Ex758BDi9Ro8v7EUd5w2FR0mK4qbjl3zJ74uRlmLEesKm6CSy3DqtFi8tWti82BJel8dbsSTXxej3WTFZwfrcftpU3GgpnPQA+pD7x/EKdmun/ei9Ehkx4Xi7THMgbbanXjg3QPISYvEf7aU47w5iYgNVeHLQ8eKnazZUg65TMD+6k5UtPbgsoUp+OZI0zBHlcbuynb8sW9e9X93VuHu07NQ1tKD1p5jXdi/+7gAZ89OwNr9dZgSHYKctEisGeO95aH38rEiOwZv7qzC4owoZMWG4u09Ne7t7+ypQUyoGqUtPdhT0Y7rlqbjytwUxIUF50Iy7Mr2gKe+KcZzGyZWCISIiFxmJYbji3tPkToMj2JX9iTbX90JeQC/GyEimkwNhl53VcRgxMTsAW/cehL++YOxVbMiIqLjXbkoBft/ezbiwoOzGxtgYvaYmYnhXLeXiGiCZid5f1laX8fBXx7w1DfF2FjcDNZqJyKamL+vL0FJcw/uPTM7aFvNbDF7gFIuk7T2LRFRoOgw2VBQZwjapAwwMXtEUWMXwjUKaJVyZMbqoFHyshIRjUVGjA4xoSooZALMNmdQD/5iV7YHPH/dIuwoa0NlmxHXLknDy1sr8Ie+uYFERDS8FVkxeOPWk1DTbsL/9tTgwXOGrncfDJiYPWTZ1GgsmxoNAIjUKSWOhojIf+hDXPfM1KiQoE/KALuyvSIzJhS/v2iW1GEQEfm81TnJuHJRyrCLgwQbJmYvmJ8agZuXZyAzVjfyzkREQezeM7OxcnpcQC9gMVZMzF5wqNaA13dUorxl5GUKiYiC2bMbSrClpIUt5gH4jtkLjjZ14zcfH5Y6DCIin/fhvjpYbE6ckh0rdSg+gy1mD9lZ3oZ3+lao6V8ajYiIRtZ/z6xpN+Gpb4oljkZ6XF3KA+56cx+2lrbC5nAiOUKL2o5erutLRDQGU2N16DLb0WmyIisuDK/9cHFAFRnh6lKTbHpCGAy9NpisDpQ09zApExGNUVmLES3dFtgcIlQKWUAl5bFiYvYAi92BWYksvE5ENFERIUrMSgwL6spfTMwe8NA5M/DC9Qu5uhQR0QTde2Y2/nLZPLaYaeIKG7q5uhQR0QQVNnBBIE6X8oAbXtqFHWVtUodBROT33ttbi/WFzfjy3lOCttXMFrMHLEiNgJ3NZSKiCRNFID5cE7RJGWBi9oiIEBWWZ0VjVmI47j9rGqbFhw7afsWiFNxzRhYUMgF3nDYVPzgpTaJIiYgm30kZUbj/rGlICNfgovlJuG9VNlSKY+lHpZDhvlXZuHBeIhL1Gpw7JwHN3cE7+Itd2R5wy4oMXDQ/ES3dFsxO0mN+agRuenk3ACAtKgT/d/k8yGUCFqZH4vTpcQCAfVUdKGrsljJsIqJJ8eSV85EaFYJF6ZHInRIJtUKOdqMV/91RBQC4ZnEq7ls1DRa7A3srO3ByVozEEUuLLWYPiQvTYHaSHgCwt6oDISo5AMBktaOqzVUzuz8pNxh60W60QuAobiIKcGFqBfKq2gEAy7NioFbI4XSKKKgzQCkXoJLLcLi+C06nCLVCHvRJGWDlL6+p7+zFk98U4+mrFgy5z4PvHcT7e2snMSoiosmTHKHF+gdOg0YpP+H29/JqIAgCrliUMsmRTb6xVP5iV7aXJEVo8dcr5g+7T1Zc6LDbiYj8WXKEdsikDCAoEvJ4sCvbi+RDVBxpMPTi/ncP4J8bSic5IiKiybO7sh23/TcP6440nXC7IAgQ+E7vOBNOzIIgpAqCsFEQhEJBEA4LgnCvJwILZIl6LYobu9FtsUsdChGRV31zpAlzU/RSh+FXPNFitgN4QBTFmQCWArhLEIRZHjhuwGow9EImCFDKBSTpNZiRECZ1SEREHpMcoXVPG82M1WF3RbvEEfmXCb9jFkWxAUBD37+7BUEoBJAM4MhEjx2oEvVafHrPCjz97VFcd1Ia4sM1uH7NLmwtbZU6NCKiCQlTK/Dx3csRE6rGr9YewqMXz4ZCzremY+HRwV+CIEwBkANg1wm23QbgNgBIS2OBDQC4/6xp7n+rFfzFJSL/J5cLUPSNr/nT6rkSR+OfPJYNBEEIBfABgPtEUTyuCrkoii+KopgrimJubGysp04bEBxOET8+NROXLUyWOhQionHLiNHhhesWweZgieKJ8EiLWRAEJVxJ+U1RFD/0xDGDiVwmYGlmNKJ1Kny4r07qcIiIxuXqxalYNjVa6jD83oQTs+Aa6/4SgEJRFJ+eeEjBx+EUsb6wCWv3MykTkf96fUcVMmJ0mJ8SgQR98C5CMVGe6MpeDuAGAGcIgnCg77/zPXDcgNdfdU0uE/DRgTp8WdAocURERONX19mLX609hFCNq80nRWXJQDDhxCyK4lZRFAVRFOeJorig778vPBFcoPvj54WoaTcBAJq7LBJHQ0Q0cUaLA91mG0RRxL3vHIDV7pQ6JL/DWtkSaDD04gf/2YWqNiP0WiW0SjnqDcG7xBkRBZbIECU0SjkaDGbEh6vxyHkzcWlOcA9uZa1sH5eod9WPdYpAh8mGDtikDomIyGM6TDag777W1GXBkowoaQPyM5w8K4EGQy9OyohCZIhS6lCIiLzqikUpONrEtefHgi1mL7LanVCdoHBIol6L3188G6lRIXjsMxZII6LAdPLUaDx55dCr7DmcxwbA0jFsMXtJVZsR97y9b9h99ld3TFI0RESTr6S5Bz3DLNbz1q4qvL27ehIj8g8c/OUFf/26CM9vKoMoukptfnXfqciI0bm3Nxh6seqpzTBaHRJGSUTkfTIB+OsV83H5gLWXnU4RFz63FUcaXEUiZyeF49O7V0AWwC3nsQz+YovZQ+o6e7GvrwW8KD0S/c87MaFqpERqAQCfHqwHAMSHaZAUoZUkTiKiyeQUXfdEAFh3pAm9VgdkMgEL0iLc+8xPjYBMJqDX6sD6whOv3RxM+I7ZA/6xoQQbipph6LXh9OlxWDfgF6uusxf3vLUf8eFq/HdnFXaUt6HbbEdJc4+EERMRTZ57/3cAi9Mj8dGBOsxLicCUaB3e3VPj3v7unhpoFHJUthmRX2vApQvacNupmYgLD87qYezK9oBn1h3FM+tKpA6DiCggzEvR45O7V0gdhkexK3uSFdQZEKKSSx0GEZHfkwlAp8mG5q7gLbrExOwBa25ajBeuXyR1GEREfu+Gpen47uenB203NsDE7DFJeg0CeEAhEdGkSI7kwFgO/vKARz89jM1HW+DkQipERBPy3PpSHKwx4HcXzQraVjNbzB4QrVOhvMUodRhERH6v22JHXWdv0CZlgInZI8pbjYgLUyNco8DCtAiEqdkRQUQ0FgtSI5AWFQKVQgalXAjqwV/MIB7w9FULsL+6A9XtJlyyIBnv7K7GLz48JHVYRER+YdXMeKy5KRdNXWas3V+HO06bKnVIkmJi9pCctEjkpLmq2zgkmBtOROSv+utpxIdrgj4pA+zK9oqTMqLx92sWSB0GEZHPu2FpOn56ZjacHD3rxsTsBVlxobhkQTIyY3Uj70xEFMR+tCLDXSubXJiYvWBneRueW1/CkdpERCN4/MtCfHqwni3mAZiYPaj/F6utx4qnvj0qcTRERL7v68NN2FjU7G4xM0EzMXvMxqJmvLq9EgBQ2caWMhHRaFW3mwAApc09eOzzIxJHIz2uLuUBP3p1D7aXtcLhFKHXqmDotcLm4FMfEdFoRetUsNidMFntSNRrsfbOkwOqyAhXl5pkc5P1MNucsDlEtPZYmJSJiMaozWhFj8UOpwhE6VQBlZTHionZA1QKGXLTI6UOg4jI78WGqXFyVnRQV/5iYvaAu07Pwt+vzeHqUkREE3Tvmdl45LyZbDHTxFjsDuwsa+PqUkREE7SzvA0WuyOoR2ezJKcH/OjVPdhW2iZ1GEREfu+z/AZsLm7BxodWIiZULXU4kmCL2QPmJkdIHQIRUcBIitAiKkQldRiSYWL2gMxYHc6eFY9F6ZF47JLZmJ+iH7T9lhUZ+M2Fs6BWyPDIeTNw58qxF2m/98xsPHTOdGiVcvzuolm4cVn6oO25A859wbxEPHbJbMSFBefTJhGN3v1nTcP9Z01DiEqORy+ejetOShu0fUlGFB67ZDZmJ4XjkgVJeOyS2YgJHVvSPHNGHB67ZDbSo0Nw3UlpePTi2QhRyd3b+8/9g5PSMCU6BNcvTUOb0eqRz+eP2JXtAVflpuLsWfFo7bEiKy4UmbGhuG7NLgBAenQIfn3BTAiCgPkpeuROiQIAbCpuwZGGrlEdf1F6JH521jQAwEkZUcidEgWnU8S3R5rQYHCNXHzk/JlYlB6JJRnRSIrQIEyjhMXuxB8/L/TCJyaiQLA0Mwo/PTMbAHDy1GjkTomC3eHE14eb0NpjAQD85oJZmJuix6L0KEyJCUGISoFuix1PfFU86vP8cfUcJOq1mJsSgTlJ4VDIZahsM+KVbZUAgKsXp+Kmk6fA7nCioL4LC1KDuxeSLWYPiQhRISsuFACwr6oDMaFqCAIgFwRUtLoqgfUn5frOXljsDshHMYxbIRPQbba5/0j6j3G0uRtqhevHFxemxr6qDgDA9IQwhGmUAICCOgPCNXz2IqLjKeUCOk02dPS1TPvvLUWN3QhVu1qzcWFq5FW1AwBmJYUjROW6nxyu60LYKO8tSXoNdle4jrEgNQIKuQxOp4iSph7oVHLoVHKUNPXA6RShkMuCPikDrPzlNa09FvxjQyl+f/HsIff57ccF+O+OqmGPc/upmXjk/JlDbv/l2kN4+JwZ0IcoT7h9X3UHLn9hO7hENBENdM8ZWXjg7OlDbv/FB/n41QUz3Q/637ervA1Xv7hz2HNkxujw9c9OhVJ+4jbgZ/n1kAkCzp+bOPrA/dRYKn+xOeUlMaHqYZMyAETrRn4HHD3Cu5w/r5477Ha9Vgm5IMDOzExEA0Tphr+3PH75vGG3R4SoIJcJcAwzrSlMq4RimJ7BC+clDR9kkGJXtgQaDL245dU9+NfmshH3fXZ9KW59bY+7K3ss/vDpEdz6Wh7sQTwfkIhO7Olvj+LW1/LcXdlj8buPC3Db63nDJmUAOFjTiWte3ImvChrHG2ZQYmKWQKJei6ZuM3ptjhH37bHY0dVrH9d8vuRIrfv9NhHRQN1mO3ptdkSO0HI+kcQILaraTKPad1dFOxam8b3xWDAxS6DB0IsIrQpqhQxZcaFYmhl13D4nT41GZqwOISo51ErZuFrM1W1GpEZpodcqsTwresSuKyIKbILgurdkxOgQqlZALpONq8Vc3W5CcoQWkSGue0vk98a4TIsPxZIM130tJy0Ce/sGp9LocPCXhNZsKcdlC1MQpVPh1tfysK6wCQBw0fwkPHdtDpq6zPiqoBE3nTxl3OcoaepGQb0Bq3NS8MWhBtz55j4PRU9E/uayhcl4+qoFqO/sxYaiZly/NH3kbxrCkfoulLX04KL5Sfj4QB3ufecAACAyRIlND54OfYgST35djAfOngZB4EICHPzlJ249JdP9b5PV7v630eL6d3y4ZkJJGQCy48OQHR8GAOgx20fYm4gCWf+9JSlCO6GkDLimT81KCgfg6hbvZ3OIsDqcAIAHzxl61DcNjV3ZPsDhFPHbi2bhpmXpuO3UTDx87gx4oydj2dRovHLzYq6CRRSE7lw5FfefNd0r95bTpsXiPzfmYmZiON7/yTKohpgeRaPDFrMPkMsEzEgIx0PnzoBCJkCjlI/8TeOQGhWC1KgQzEwMx+H60VUdI6LAcMOydCTqtV45dv+9ZUFqBGJZCnjC+FjjQ0LVCq8lZQDYUdaGZ9YdZVImCkK//+Qw1u6v9UqLuR+TsmcwMQcBe9/7HpvDiWfWlUgcDRFJ4evDTThQ3ekeiNV/XyDfw8Qc4LaUtOAfG0sBAMWN3RJHQ0RSKm5y3QMO1nTiL18WSRwNDYXTpQLY7a/nYWNRC+xOJ3QqBUw2x4iVeogosIWpFbA4nLA5nEgI1+CLn54yriIjNDZjmS7FFnMAW5AaCavDCacIdFvsTMpEhG6LHVa7E6IIpERqmZR9EBNzAAtRyXHqtFipwyAiH5QapcXyrJhxVf4i72JiDmA3nTwFz1y9gHMKieg4D549HfetmsYWsw/iHTuAdZlt2Hy02V2Fh4io36biFhh6bRyd7YOYmAPYw+/n42f/Oyh1GETkg9bur8OZT20a1Sp3NLmYmANYf41sIqITSdRrEapmAUhfw8QcwKbFh+KynGQsTIvAE1fMO255yR+fkoHHL5sLnUqOX18wEw+fO0OiSInIEy6en4QnrpiHlEgtbj8tE39ePRdqxbHbfESIEk9cPg/XnZSGGQlhuGFZOgy9NgkjphPho1IAu3BeEs6YEYf6zl5kxYUhOy4Uq5/fDgBICNfgl+fPhCAImJUUjnkproXMNxQ1YU8l104l8jdymYBHL56NSJ0KMxPCMSc5HIIg4FBdJ97eXQMAuP6kdFy1OBWrFyajtLkHMxPDJY6aToQt5gAXolIgK87Vpb2vuhPJEa4i9hEhShT1VQLrT8qtPRaYbU6O4ibyQ+lRIdhT2Q4AmJuihyAIsDmcqG43IUytQESIEiXN3RBFEUq5jEnZh7HyV5DpMtvwt2+P4ncXzR5ynye/LnaX8SQi3zcnORyf3LUCsiHWdP34QB1iQtVYnhUzyZFRv7FU/mJXdpAJ1yiHTcoAEKL23gpXROR5GoV8yKQMAJcsSJ7EaGii2GdJbq09Fly/Zhf+sYGtZSJ/klfVgcue34b1hU1Sh0IewMRMbjGhaljtTpisnNdI5G8O1hqQmx418o7k85iYya21x4JwrRKhagVmJIThjBlxEIbuHSMiic1P0WN5VjQAYGlmFPZVc0ZFIPDIO2ZBEM4F8HcAcgBrRFF83BPHpckVE6rGmpty8c7uapw5Mx6xYWrc/+4BfLivTurQiOh7EvUavHPbMmhVcjy3vgT3nJktdUjkIRNuMQuCIAfwTwDnAZgF4FpBEGZN9LgknWuWpCE2TA0AaOm2SBwNEZ2I0WKHyWoHACblAOOJruwlAEpFUSwXRdEK4B0Al3jguCQxURTx2CVzcM8ZWVKHQkQDLEqPxCd3r4BGyRkUgcgTiTkZQM2A/6/t+9oggiDcJghCniAIeS0tLR44LXmbIAiYEqPDlYtSpQ6FiAa4cF4ipsTooGOd64DkiZ/qiYYHHVe1RBTFFwG8CLgKjHjgvORloiji7d01+Ppwo9ShENEA/95cDrlMwFmz4pGo10odDnmYJ1rMtQAGNqlSANR74LgkEVvf+qyCIKCwoQubj7KHg8iXNHaZ8dLWCsSGusaC2LimckDxRGLeAyBbEIQMQRBUAK4B8IkHjksS+e3HBciv7YQoisivM0gdDhGdQFOXGY1dZphtDtz40m6Yua5ywPBIrWxBEM4H8Axc06VeFkXxT8Ptz1rZvqm1x4JVT29Gp8kGmQDIBAF2J986EPkquUyAAMDuFCGXCfjHtTk4b26i1GHRCUx6rWxRFL8A8IUnjkXSiQlVIy0qBJ0mA5wi4JRggRMiGj3H9x6cc9IiJYqEPImVv8itrceC8+cmIi0qROpQiGgMZAJwzxlZqDf0Sh0KeQATcxDqMttO+PXoUDXuOG0q7mWxAiK/ct6cRNy3ahoWDtFi7rU6YOcAMb/BxBxEOk1WHKjpxI0v7UZzlxm931uswmJ3oLnLjG+OcHoUkT/ZWd6G6jYTur/30O1wimjuMuOZ9Ufx0tYKNHeZ4YlxReRdTMxB5KP9dbj0n9twoKYTK57YeFy5TYvdifOf3YKvD3PpOCJ/0ma04tS/bsTBmsGzKAQAP3ptD/69uRx/+bIID76fD4Er0/g8JuYAV91mchcISYs+9u44VK1ApE4JAHhpawVEUYROpUCUTiVJnEQ0MXKZgMQIDQDg7d3VMFrskMkEJEccK0CSGun6d0u3BR8f4OI0vsoj06XGitOlJsf/fVWEDYXNaDdZcVJGFPZWdaDBYHZvz0mLQKJegy8ONeK0abFwOEVsLW2VMGIimojsuFBMTwjD+sJmzEoKR2yoGl8NqNwnlwk4d04CqtqMqO3oxanZsXj04tmI5AO51036dCnyTTGhahQ3dQMAPstvOG77/upO7O/7N6t7Efm/kuYelDT3AAD2Vh2/NrPDKeLzAfeC1h4Lk7IPYld2ACtt7kZ8uFrqMIjIB2mUMtgdIjqMVqlDoe9hYg5gf7lsHl65eQlkHOtBRN/z0zOz8e4dy9hi9kFMzEGAozCJiPwHE3MAe+TDQ/jRq3uOK9tHRPTc+lJc9e8d7Mr2QUzMASwrLhSNXeaRdySioNNrc0AhE9iV7YOYmANYc7cZ0+JDEROqxgVzE5Go10gdEhFJSC4TcP7cBMxOCkdEiBJROhVbzD6I06UC2CPnzcR1S9JR2NiFc2YnYENRE370KuePEwWrqxen4s+r56Kl24Jtpa24NCdZ6pDoBJiYA1xadIi74ldNO1eeIQpmNe0mAEBsmJpJ2YexKzuIXDw/CWvvPBlKOUdpEwWbX5w3A09eOZ+LWPgBJmYfUtFqRF2n91q1kToVctIiMTU21GvnICLfdNH8JMSHa7w2fVIURWxnSV+PYFe2D3A4RbyyrQJfH26EQibD2bPjcfPJUzz+B7SxqBmbj7agqLHbo8clIt/3iw/ysWpmPG5clu7xe8u6I03YfLQFn+bX4yenTcW5cxKQHq3z6DmCCVvMEjLbXOshy2UCSpp6sKeyAzvK21DVZnL/4fTv44nzaJRyvLq9csLHIyL/s6WkFbUdnr23WOyuY6gUMry+swqdJhte216JRL3WY+cIRkzMEhBFEXaHEw+8dxAFdQZY7A7sqz5WcD6vqh02hxN7q9rxq7UFsDuc43ov5HCK+PpwI/727VHYHU7srmj35McgIj+zu7IDNocTO8ra8PtPDo/73mJ3OPHpwXo8t770uHtLc7cF5a09MNscuH7NLhhMNjhZ5GhMuOyjBBoNZpzx1CaYrKN7mgzTKLDpwZWIDh3bghS3vLoH64uaxxMiEQWBiBAlNj24EhEhYysycsNLu7ClZPTvk/929XyszkkZa3gBZSzLPrLFLIHYMPWYin2kRYUgahzVeeanRoz5e4goeKRH68aclAFgwRjvLfNTeC8aCyZmCbT1WHDD0nQkR2hH3DcjRocrF6WgfRzVeZIitDh/bsJ4QiSiAJcVF4rVC5LQaRr7vSU1MgRnz4ofcT9BAB46Zzo6xnGOYMZR2V7UabKe8Gk0LlyDm5dnQKmQ4VdrC4Y9xk/PzBq2C2iocwDAFYtScNaseKw70gyrwzm24IkooN23KhsXzksacvtw95arFqdi1ax4rC9aN+wiOWdMj8Ndp2cNuX3gwFQ6hi1mL2g0mLH5aAt++OoeVLUZ0WOxD9pustpR1WbE5/kNIx7rkwP1qGozHje6sdtsQ1WbEde/tAs7ytrQfILFKmraTfhwXy2TMhEdZ6h7S1ffveWaF3diT2U7mruPv7dUt5nwwd7aEVeu21XRjsP1BrT1WAZ93ekUUdVmxDPrSvD39SWoajNygNgAHPzlBWu2lOOPnxcCAELVCmx48DTEhR17p2ww2bDyyY3oMNlGdby4MDU2PrgSOvWxDo7aDhNWPb0ZZpsr6f7x0jm4fmn6oO+7/90D+HBf3UQ/DhEFqES9BhseWAmt6liLtarNiLP+9h2sdte95Ykr5uGq3NRB33f3W/vw2SgaFv1euXkxTp8RN+hr17y4AzvLXaO5l2VG4+3blo73Y/gFDv6SQFFjF9YdaQLgGtzVT6uSQ6dyJdR/biwFAGhUMoRqRv8WIUyjgFohgyiK7mOEqZXQDuj+6T/n5/kNKG/pOS4OIqLvC9cooVLI4HCKeH5T371Fo4RGcSw1xPbNBvn4QJ271vbAhsZoxPQd481dVe7xMrEDjhHTd69qN1rx5q6qcX6awMEWswf85qMCfFfSApPVgfkpeuyv7kTbgMFa81L0iAhR4bujLTh5ajR6bQ7sr+4c0zly0yMhlwnYVdGOldNj0dJtweH6Lvf22DC1+9x6rRKZsTp8V9LqfuolIjqRJVOiAAHYXdGOM2bEocFgRmHDsXtLfLgac5P12FvVgSidChkxOmw+2vhx87QAACAASURBVAKbY/S5IzVKi+nxYdhR1oa0aJ2rpf69qZz9565uM2LZ1Bj8efUcxIUHzlK1Y2kxMzF7wD82lODJb45KHQYRUUDISYvA2juXSx2GR7Ere5JVt5vGNC+ZiIhOTKOUQauUn3BAa7BgYvaAJ66YjxdvyIWXFm0hIgoaPzktC2/9eGlAdWOPFROzh/TaHOAyp0REE9PLhS9YYMQTfvr2fmzlOqRERBP20tZybCttxUs35QZtq5ktZg/IjgsdV8lMIiIazOYQIZMJQZuUASZmj+ix2jEjIQzJEVpcvjCFA8GIiMZAJgCX5SRj8ZRI6LVKZMeFBvXgL3Zle8Aj583E1bmpaDCYsTwrBl8VNOCON/ZJHRYRkV+4bGEKnrxyPrrMNmwsasYlC5KlDklSTMwekhkbiszYUADA0aYeiaMhIvIfZS09EEUR4Rpl0CdlgF3ZXvGjFRn4+K7lkMs4f4qIaDg/P3c6Xr15idRh+BQmZi8IVSswN1mP1MiR11smIgpmq2bGQx+ihMBCEG5MzF7wxaEG/PyDfFS2maQOhYjIp/3ig3y8sKmMyz4OwMTsIaIoutc1DVHJ8f7eWokjIiLyffuqO1Hf2QtZ36s/k9U+wncEPiZmD+ix2PFuXg3+vr4EPRY7Nh9tkTokIiK/saO8DV1mG3aWt+HB9w6ix2KH3RG8K+NxdSkPuPHl3fiOyZiIyCPCNQpseuh0ROlUUofiMVxdapLNSAiTOgQiooCRoNcgQquUOgzJMDF7wKzEcFwwN1HqMIiI/F5mjA63rMhAuyl4yxyzwIgHXJqTjBXZMfiyoAEcWEhENH4/WTkVV+amSh2GpNhi9oCjTd14c2c1kzIR0QR9uK8OxY3dMFqCd3Q2E7MHPLehFH9bd1TqMIiI/N6O8jbc8NIuqcOQFBOzB4Sq+UaAiMhT1EoZFPLgrQTGjOIBF81LhEouoLHLjOuXpuPlrRXYWHxs+tRjl8xGcqQWD72Xjz9fNheGXht+/n6+hBETEU2eH5yUhvPmJOCJr4pxwbxEzEwMx8/+d8C9jn20ToWnr16AI/Vd+LKgAQ+dMx29VgfUCrnEkUuDidkDTs6KwcL0SHT12hAXroFcJrgTc0aMDjcsmwIA+PDOk5EerQMA/HdHJQrquiSKmIho8tx3ZjbiwjVIj9IhLToEALA6Jxkvba1w//u0abE4bVosLpib6N4nWLEr20M0SjniwjUAgOLGbsxJDodSLiA9OgTlLa5lIPuTcoOhF7GhamiUvPxEFNiWTInCvuoOAHAnXKdTRIfRioRwDRL1GrQbre5a2cGelAFW/vIao8WOt3ZV48enZg65zzPrjuKZdSWTGBUR0eSZn6LHR3ctH3LlqB1lbRAEYGlm9CRHNvnGUvmLXdleolMrhk3KANzvV4iIAlGX2Q67U4RyiIFcy6YGfkIeD/alSqDB0ItVT2/Gm7uqpQ6FiMhrKlqNWPnXTfj4QJ3UofgVJmYJJOq1UMllcLAiCREFuLrOXizJiJI6DL/CxCyBBkMvctIioNcqcUp2DK5dkgpZ8E7ZI6IAc+q0WFydmwpBAC5dkISixm6pQ/IrfMcsgUS9Fn9aPRf/396dh0dV3Y8ff5+ZzGSy7yvZE0II+xZ2EAFFXLG1LojWXSu0/Vprtdpv+2tta6utti61fsXWtS3iWlxxQ1EBw76FnRBCIAvZ95m5vz9mMhAhySSZ5M4kn9fz+Dwm9+aezz3MzGfOuWeZmhnF3Jw4AsxGWqwar206qndoQgjRK/GhFp5ZMgGLycjEtIhBv+51T0iLWUcXjU4kwGzEZtfYVSJzmoUQvq+ivpljVY0AkpR7SBKzFzAoeOMH07jvghy9QxFCiB6bkhHJ9l+d71qzQfRMrxKzUuphpVSBUmqbUuoNpVS4pwIbTJRSWExGpmVG6x2KEEL02JSMKCwmI0YZNNMrvW0xrwZGapo2GtgL3Nf7kAYfm13j0dV7ufd1WT9bCOG7nv3iEH94v4BD5fV6h+LTepWYNU37UNO0tk0z1wFJvQ9p8KhtagXAaFCU1zWz85g8ZxZC+K66ZitvbzlGSqRjWc22zzjRPZ58xnwj8F5HB5VStyql8pVS+WVlZR2dNig0W22U1Taz7F+b+WJfGSdqmvhiX7neYQkhRK+VVDey4dBJSqobueLprzlcXi8Jupu6XCtbKfUREH+WQ/drmvaW85z7gYnA5Zobi28PhrWyO1NR18w5j3xGbZO165OFEMLHPX3tBBaMPFsaGTw8ula2pmnzuijseuAiYK47SVlAaICJqCCzJGYhxKCQFi07RnVHb0dlLwB+BlyiaVqDZ0Ia+KoaWvmf+dlkxMiUAiHEwGU0KH67aCRWm7TZuqO3z5ifAEKA1UqpLUqppz0Q04BxvLrprL+PCfHn0rFDuHVm57tPCSGEL5szLJbFk1MZOSTsrMfrmq3UNUvP4bf1aklOTdOyPBXIQHKgrI4dxdX87bMD/OE7o0mNCiQ80Ow6XtPUyqGyev6TX6RjlEII0bfW7i/jy/3lpEYFkhRxqjvbbtfYcayaFflFKBRXTExiZGIYBpn/DMjKX33i6wMV/OjfWyg4Xsv1/9hwxnGjUtzyQj6bj1TpEJ0QQvSPplY7i59dz9HKxna/NxgUj67ey0vrjvDiukIe+2ifJOXTSGL2kC1FVa49R/1Oe4EpQOH4+cFVu9A0DaXAoORFKIQYHNpWAnt6zQFKa5qcvzOccfxETRN/X3Og/wP0Ml1Ol+oLA2261N2vbuWLfWU0tdoZGhvMnhO17UZcZ8QEEeLvx9aj1YwcEkpzq519pXU6RiyEEP0nJsSf1MhAthVXkxBmISrIzKZv9RiOTwmnor6F49VNjEkO54mrxxEbatEpYs/z6HQp0bW0qEBWbmwGIL+w8ozjB8tOLU+3o1hW9xJCDC5ltc2U1To+IwsrGiisOHMSz+mJutVmH1BJubukK9sDyutaZOqTEEJ4QLC/HwlhFleX92AkLWYP+NUlI9h7opYFj32OXabrCSFEjy07N4vbZmfqHYaupMXsISXVTZKUhRCil45VNXZ90gAnLWYPuPn5fL46IJtQCCFEb720/gif7S3j1dumDtrnzNJi9oCRQ0JpaLHpHYYQQvg8m10jLMA0aJMySGL2CKNSjEsJJzMmiJtmpJMaJQu2CyGEu/wMihumpzFnWAzRwWYmpUXK4C/RO8vmDuXyCUkcr25iQmoE0zKjuOn5gTNPWwgh+tL3JiXzy4tH0Nhi48v95czLjdM7JF1JYvaQIeEBDAkPAGDjWeYyCyGEOLstR6qw2zUCzMZBn5RBurL7xD0Lcljz03MwG6V6hRCiM7+5bCTv/mimrJV9GskcfSQxPIC4MH+9wxBCCK82PiVc7xC8jiTmPvDaxqPc+fImik7KfDwhhOjM3a9u44/vF2CXhSBcJDF7iM2uuTb8jgu18OGuEzpHJIQQ3m93SQ0NLTZXV3Z1Q6vOEelPErMHHK1sYPnag/zhvQKKTjbw9tZivUMSQgif8UlBKUcqGnh/Rwk//Pdmik420NQ6eNeGkG0fPeC2F/P5YKe0kIUQwhOigsx8+tNzCLWY9A7FY7qz7aO0mD0gfhCvUCOEEJ4WGmAiwGTUOwzdSGL2gFnZMVw5MVnvMIQQwuflxIfwswU51DZZ9Q5FN7LAiAfMHR7HuJQIXt1YJDtMCSFEL9wyM4MFI+P1DkNXkpg9IP/wSd7fcVySshBC9NJL6wtJigggJyGUsICB84y5O6Qr2wNezT/Ks2sP6R2GEEL4vM1HqrhrxVb8/QZvehq8d+5BzdbBO6xfCCE8zWbXsOswY8hbSGL2gFtmZbB0ThaXjxvCqmUzuHB0Qrvjj189jtd/MI0h4QGsuG0q/3fdRLqzLKyfQfHPGybxys2TSYoI4M07p/PIFWPanXP5eGfZoxL4yfxsVi2bQW5CqCduz6MyYoJYtWwG9y8cztycWFYtm8E1k1PanfPgZSP579IZpEYFuu7b3M1vz09cM47X7nDU+au3T+XvSyagTqvztrKvzkvh5hnprFo2g6kZUZ64RaGjEH8/Xr19Kk9fO4HsuGBWLZvBfRfktDvn1lkZrFo2g8npkfz+8lG8vXQ6CWHdm1lxz4JhrFo2g5z4EP62eDyv3TGVEMupJ4NDYx1l33tBDvNz41i1bAZXTfK+AaJmo4EXb8rjxZvySI4M4O2l03no8lHtzrlyYjKrls3gvNw4frYgh1XLZpAdF9ytcm6b7ajzvLRIHnLW+emzWeJDLa6y89Ijefb6idgG8bNBecbsASMSwxgaG0J9s5WIIDOLJ6fwzrYSANKiArl4TCIAb/xgmmvz79zEUHYU17h1/XEp4ZwzLBaA1+9wXGNMUhh/+nAPJdWOPUuXTEll5JAwfn3pCMIDzRgNiu9MSGLXql2evt1eWTR2CCOHhDEiMZTLxg0hJsSf709L45X1RwCICDRxdV4KRoPi1dumuuprakYUa/aWuVVGRnQQF40+s86Hx4eyq8RR51fnpTBySBj3LczBbDRgMRm5clIyXx+s8PQti350Tk4sk9IiAccazLGhFjJjgnn0o700tdoBuGF6GglhATx+9TjXa+Oi0Qn83xfuPY4y+xm4fmoaQf5+vHBjnusac3NieXPLMQAWjXe8znMTQqkY30JMiD9Lpqby72+KPH3LvTIxLYKZQ2MAeO12x3tlZGIYj3y4l/K6ZgCWTHV8tvx20SiigswYDIpF45L4w/sFbpdz4/R04kItPHHNqTq/cHQCy52PAC8cncDopHBGJ4Vzbk6s65zBSlrMHmL2MxARZAbgcHkDUzOisJgMjEuJ4FB5PYDrxVZS3Uh2bAgh/l1/LwoLMJEcEeh6k7RdY19pHVMyojAbDczIimZ/aR0AUcH+GJ3N8cr6FoZ7Uas5Oy6YWueypUopYkIcm3wUHK9ldnYMRoNidnYMu445kmfbvVY3thIVbCbKWb+dCTAZGZMcTmFF+zo/VtXIsPgQgv39yEuL5GhlAwChFhMW53zJkuomxibLgvq+Kj7UQoDJQGOL49FS27/99uJq5g13bCU4b3ic6wtx23G7XaPFaic9Ositcs7LjWN7cXW7azS12vD3M5IYZiEnPoTqRseykgbDqdf5vhN1zMqO8cStekRkkJn4MAuV9S3AqXspOF7LzKHR+BkUM4dGs6+0FoCYEP9Ty2Y2tpITH+JWOfNz49h+tH192e0arTY7aVGBpEcH0Wqzu9bKHuxJGWTlrz7T1GrjrS3FXDkppcNznl5zgIfe6/xb5/+7ZATXT0vr8PjL6wv53sRkTB1sMXm0soE5j3xGq03fbiGDgtV3zSYz5uxdYHa7xssbjrBkSmqH11iRX8Q9K7d1Ws4Pz83irvOGdXh85cajXDAynqAOvhRV1rcw64+fur5ACN/x9LUTOp1m88LXh7lualqHx7cWVXHpk192WsaCEfE8vWRCh8fX7C0jJTKwwyRvs2uc/9jnri/SevrdolFnPEY63UvrCl29V2dzuLyeuX9e02mXc15aJCtun9rh8S1FVShgzCD4Qtydlb+kK7uPOLpGO37RAxxw4815oKzzcxZP7jiRARypaNA9KQPYNThUVt9hYjYYVKdJGbquC4ADzt6Jjnx3QlKnx0uqm6hvkaTsiw6Wd/766Cwpg3uvr8MV9djsWofJanYXLeKaxlbKapu7LKc/HOzifq/t4v3YVhedKa5qpKnV5uqV+jbpoTo7aTHroKS6kcue/JLS2ma6qn6DgoSwAN5aOp3o4O7t73zHSxv5pKCUZqu9F9F6jtnPwLTMKP55Q163/q66sZWLH19LcVWjWwNCooP9WXn7VNLc7Jpsc9/r23lzczGNg3jxfF/mZ1CkRwfx9tIZBJjdX87Rbte48pmv2VpUTYut6/dKWICJv1w11jXuw13PfnGQxz7a59qFTm8G5dg3/r9LZ7gew7nr5ue/4Yt95W59toRY/Pj1pSNYNK7zL8UDnayV7eUSwgKICDR3mZTB0dKMCfHvdlIGR/eQtyRlgBarndFJ3f+GHBZgIikiwO1RmkH+RlIiA7tdzviUcEnKPsxq18iMCe5WUgZHb82IxDC3kjJAY6uNcckR3Y5vUlqk1yRlcHy2JIYFdDspA4xJcv+zpbbJysTUyG6XMZhJYtbB8eom5g2PIzrYn4Wj4rnjnEz8TusaM/sZuHNOJvNz40gIszAjK9o1+Ks72gaGZcQEsXROltuDNTwp87Sy89IjCermhyY4WswTUiNIjgxgzrAYls7JIvC06xiUYwrMpWMTCQswcfHoRIqrGrtdjtWuMW94HIlhFpbOyZJuNh8QEWhi6ZwspmVGkRUbTHZcsGvwl7vsdo3oYDNjksMZkxTG0jlZJEUEtDvnexOT+P60NIwGxQ3T07rsNj+b0tpmrpyYjMXkeH/Pzelei9sTTi97SHgAkzMiXYO/uncdI1MzohgaG8zSOVkMjW3/iOqi0QncNjsDo0Hx/WlpHDnZ4KlbGBSkK1tHn+4pZVpmFP5+Ru57fRv/2uCYSnHD9DR+efEIGlts5BeedE1n6ImKumYOV9QzITWS9QcruPKZdZ4K3y3/uGESc4bFsu1oFbEhFuK7OV/0dF8dKGfkkDBCLSYe/qCAJz89AMAlYxL569XjsNk1PttTylznCNyeaGixsrGwkplDY9hfWse8P6/p8bVE3/v5whxunZVJWW0zRZUNjE/pfku2zcGyOuyaRlZsCG9tKeZH/94CwPCEUN770UwAPth5nPNH9G4d5w93Hue8EfHUN1uZ8vuP+3WzhttmZXDfwuHUN1vZUlTF9KzoHl+rtKaJ4qpGxqVEsHZfOdcuXw/AkPAAPvvpOZiMBo/U10Ahg798xBznMyqbXWN7cbVrQMnWoio0TSPAbOxVUgbH9KkoZzd4fmElZj8DLf3UvW02Gth4uJI5w2J71IX9bdMyT32IbCmqws+gsGsau0pqaLY6pqv0JikDBJr9XHWef/gk/n4Gr3ocIE7xMyg2FVYBjsc9bdOSeirjtIGJmworMRkVrTaNEzVNnKhpIi7U4pEkc57zGrtLarD248BMo0Gx2fnZEuTv16ukDI5pTW1TmzYWVmI2Gmix2alpbKWwop6s2BBJyj0kLWYv8uK6QgJMxi5HDvdGaU0Tcx75jPpudvd1l7+fgY/umk1yD571uuvd7SUcrWzg1lmZfVZGbVMrcx75jPK67nf3ib71ys2TmdbL5NKZPcdr+ceXh3joO6P7rAxN07jsyS/Z6pzn25f+evU4LnEudtQXjlU18uA7u3hqccfTyQYzaTH7qGvyUrq1VGdPRAX7Exdm4WBZ59OKeis62J+4Pl4oYOGoBKxuDtjpqRCLieTIQEnMXsbsZyAztnvLQnbXsPgQHrxsZJ+WoZQiJz60XxJzbkLfjjFJDA/gr1eN69MyBgsZ/OVFjAaFUn2Xmd/YfJRbX8jv86QMjvmLt7yQz4o+XoLQr4OFVTzhw53HufPlTWw+UtVnZYieabHaufXFjTz+8b4+LacvX19bi6r42cptrNx0tM/KON3SVzbz0HsF9GUvaV/W12AitTjA2e2aa9RlalQQHxeU9lvZa/aWMcQ5urW6sbXPW7eeUuEcAZ8TH8o720t0jkZ0ZGtRFcHOjSNsdo2qBt/o1Wh7fQ1PCOXdHSX9tllDwfFa/E778l/Rg5keon9IYh7ADpbVsXztIX71353sL63l1fz+X0D/1fwi9pfW8vt3d/PUZwe8YinCjhRW1LMiv4i7Vmxlf2ktr2w4ondIogtvbTnG/tJanvn8IL9etYv9pbX9Nrixu0qqG/lqfzlXPbOOfSdqeX3T0X4dkQ3wzvYS9p2o5V8bjnDPym3sL63t9vQy0fdk8NcA9tNXt/Lqxv7pJnPXhaMTePKa8XqHcVYPvVfA02sO6B2G6IWc+BDe+eHMDpfM1NPrm45y14qteofRzpDwAD7+yewOl8wUniMrfwnAsWKWt/HGmNp4c2zCPSEWP69MyuCdr69As7Hd4kbCO0hiHsDOHR7LzTPS9Q7D5dopKSwcmaB3GB2alBbBXfOz9Q5D9NCcYTEsPXcoTV66rGpCWAAPf3c0fTi+s1smpkbwwEW5NHhpfQ1mkpgHsGmZ0fx4fjZmLxgpaVDwP/OymTG07+ad9tbEtEjunJPllS0b0bXbZ2cyOzvGa7tlcxNDuWJistcs9XrTjHRmZ8cQapHXu7eRecwD2FcHyvl4d6nbi/P3JbsGj360lwUjErw2OX9z+CRf7a9wbXIvfMvTaw7Q2GpjSkaUVybnXcdq2HGsmi1F3jH9bvnaQwSYjYxPjZDk7GX0b0qJPvNpQSnL1x7SOwyXl9Yd4d0d3jv9KP9wJY9+tFfvMEQPfbqnjKc+PeCVSRkco7LvWbnNrV3l+kN+YSW/fWc3QWZpn3kbScwDWFWD97X8vLk16s2xCffUNLX227zg7vLG11dDi41WL+hRE+3JV6UB7I5zMsmOC2HnsWqWnpvF8rWHXDtYATxxzTiGxoZw+0sbeeSKMbTa7Fy3fIPbXd9Gg+KfN0wiLMDEsn9t5qnF4zlW1cStL+a7WgWXjU1k6blZPPvFIRLDA1g4ynsHf12dl0xGTBDvbCvhFxcNZ+XG4nbTpx68bCRTMiJZ+spm7r9wOKEWE4ufXe9Ve+wONL9bNIq89AjufHkzv7golxCLX7s6H5scziNXjGb1rlL2ldbyg3Mysdk1rxyZPTUzildunswv397JU4vHs7Gwkntf3+46fvOMdK7KS+ah9wqYOzyOSWkR3Prixm6t1PfT84dx/og4fvbadm6ZmU5qVBBLlm9wbRubER3E35dMYGNhJat3neC+hTle04IXp8g85gHObteoaWolPNDMpiOVXP7UVwDEh1r46t5zMRgUFXXNrh2ovvu3r8gvrHTr2rkJobzr3A7v9GvMeeQzDpU7Pkxevnky07OiqW5sJchs9Ikl+9ru5UhFA7Me/hSAILORjb+Yj8VkbHevt72Yzwc7T+gZ7oAV7O9H/gPzzqjzW17IZ/UuR53/8uJcbpiejs2uUddkJSzQ+5+Vtt1Li9XOhAdXuxYZ+fgns8mMCeZkfQuRQWYA/vThHh7/ZL9b1zUaFN/cP4/IIHO7+rr3tW3827k07tI5Wdx9/jCAduWIvifzmIWLwaAID3S8+Qor6pmbE4vZz8CcnBj2lzlW4Wp7A5fXNTM0LoQoN96soRY/xiSHcayqsd01DpfXMyUjkkCzkdnZMRRXOo6HBZh8IinDqXspOF7DhaMSMCjHwii7SmraHa9vthId7E9iL/aYFmdnULBwVDy7v1Xndc1WYkP8SQizMCY5nDpnUjMalE8kZTh1L7tLalg4MgGjQXHByHj2nagFaJcsW20aOfHubT5xyZhEdh1rX1+tNjsWk5H06CBy4kNotdtda2VLUvZe0mIeZGx2jTc2F3e6teRL6wp54M0dnV7n/oXDuWVWRofH39h8lAtHJWL2841k3Jn/fHOEKyeldHj8/R0l3P7Spn6MaOC7YkISD18xpsPjn+4pJTchtM93MOsPK74p4nuTkjs8vr+0jvmPrum0y/ncnFie+/6kDo9vLDxJWICZrD7ekUt0TLZ9FB0yGlSX+z3vdH7r7vyczrepWzSu7/aU7m+dJWVwr75E9+w+XoPdrmHo4FnxnGGx/RxR3+ksKQPsKqnp8jnw/tI6mq02/P3OPiJ9QmpkT8MTOpAWs3Apr2tmwWOfU1Hf4taAkPBAE+/+cCaJ4QF9H5wXqm+2cv5jn1Nc1SgDaPqAv5+BF27MY3JGlN6h6ObKv39NfmGlWyPN/f0MPHblWC7w4gGWg5k8YxY9Eh3sz5DwALeTTNuzvsEqyN+P7LgQScp9xGQ0kJsYqncYuhqbEu729C+rXWN8akQfRyT6gyRm4VJe18z83DiSIgK4cHQCP5mfjcV06iViMip+PG8oi8YNIS7Un0vGJHKiZvDu6VrfbGVCagTZccGcmxPL3edlE+EjA5C81Y3T07l5Rjpmo4FbZmZw5GSD3iHpKibYn6kZUYxOCuPu87LPeEZ8dV4yS+dkYTQo7pidydHKwV1fA4U8YxYu0cH+LD13KBPTIhmbHI7FZKSqsdW1eth3JyTz43nZtNrsbDh0kulZ3rm0Zn8J8vfjzjlZTM+KJi0qkPBAMwaD4o/v79E7NJ80MTWC/704F4DpQ6MH1HPknrp5ZgYXjk6gptHKsPgQcuJDufkFx2PAjJggfrdoFEopJqRFSH0NINJiFmdoW2tY0zR2FFdjNhowGRW7Smqw2uyYjIZBn5RPNzY53DUlbWtRFf4DYCR6fzMoKKlu4mR9CzCwBnf1VkJYAMOcU6Y2Hakk0OwY4FXbZOWoczqi1NfAIoO/RJfe2lJMY4uNq/I6H50sHL7cX87iZ9frHYZPuXF6uqu1LDpXdLKBJz7Zzx++O1rvUEQ3yHQp4VGXjh2C3UvXH/ZGKZGBmIyKVpvUmbsyYoL0DsFnJEcG8vvLR+kdhuhDkpiFWzqaTyrae/iDAj4pKJOk3E1/+nAPmworeeCiXFmRyg3yfhzYPPIwTCl1t1JKU0rJg0cx6JTXNbumtEQEml3LSAr3VTa0cqy60ZWUS2uadI5ICP30usWslEoG5gNHeh+OEL6jqdXGvhN1PPXZfkYkhjI7O5aVG4/qHZbP2lhYyZq9ZRgU3Pf6dp64ZjyJ4RZiQwbvXHkxOPV68JdSaiXwG+AtYKKmaeVd/Y0M/hIDgc2uceFfv6DgeK3eoQxIJqPigx/PIiNG1ncWvq/fVv5SSl0CFGuattWNc29VSuUrpfLLysp6U6wQXkHhWJ1K9A2llNSvGJS6fNUrpT5SSu04y3+XAvcD/+tOQZqmPaNp2kRN0ybGxMT0Nm4hdNdstfPAhcOZmyNzSD0tOtjMP2+YhJ9RBjmJwafLZ8yaps072++VUqOArAs8PwAAEqlJREFUdGCrUgogCdiklMrTNO24R6MUQkf7TtQyNO7MPXEDzEYmZ0RhtWt8XFCqQ2QD1yVjhjAts+OxpIUV9cSHWTrcTUkIX9bjwV+apm0HXE0FpdRh3HzGLIQv2Hmsmp3FNfzuvd38btEochNCSYs+Nd+2ocXK53vLef6rw/oFOUCt3FjExLQIRiSGkhp1qs7rm618sa+cf3x5iJz4EObkxHKOrHolBhh5gCPEt7QNiGy22rnntW1UNbRy14ot+Ds39Gg7Hmj2Y/nag3x9sEK3WAeqmiYrP1mxFYvJ0SJuq/Mgfz/+74uDrD90kue/LiT/cKXrb/RYxVCIvuCxxKxpWpq0loWv23eiloc/cGxCcbz61FzaFqvdtY7zPSu3UdXQQqvNTnldiy5xDgYttlN1/tOV26huaKXFaqei7tSOZsed853XHaxwbbYihK+TtbKFcPrft3bw5uZiGlpsxIVaqGxooaHF5joeYvEj1GKiuKqRqCAzRoOitHbwbnvZH0ItfoR0UudKQWJYANWNrbTY7AyLC2HFbVMJMMuzZ+FdZK1sIXpgTFI4L3xdCEBxVeMZx2ubrNQ2WQGoqJeWcn+oabJS00mda1r7f6uUyEBJysLnyTNmIZzqmq3MHCqryvqqtKhAEsIsNJ7WyyGEL5LELITT9dPSeGrxeEIt0pHki3558QgeuChXWszC50liFuI0+0rrqG226h2G6IFNRyq7PkkIHyCJWQinu1/dyuVPfYXMuvFNj3+yn+kPfSJd2cLnSWIWwmlCaoTeIYheGp4QKl3ZwudJYhbCKdBsZNG4IaRFBfLAhcOZnhWld0iiCzEh/jxw4XDmDY9j5JBQpmRESotZ+DyZxyzEaZqtNnYU1zAhNYLD5fWc88hneockOvGLi3K5aUY61Q2tFFc1kpsYqndIQpyVzGMWoof8/YyuLu0Nh08SFWSWOcteKtjfj21HqwAICzQRFmjSOSIhPEO6soXowPcmJrP2Z+eSGGbROxTxLUrB8zfm8ZerxukdihAeJ4lZiE5YTAYig816hyG+xWQwEBvir3cYQvQJScxCdOCdbSVcu3w9O4pr9A5FfEuLzc6N//yGh94r0DsUITxOErMQ33LMufby6KQwvtwvWzp6q32ldSQ4HzO02uyU1jZ18RdC+AYZ/CWE047iar4+UMHq3Sf46fnDeHNzsd4hiS78a8MRchND+aSglINlddw8M4NRQ8Jc+zgL4YtkupQQTn/9eB9/Xr1X7zBEL4xNDueNH0xDKaV3KEK0053pUtKVLYST1WbXOwTRSza7rKcqfJ8kZiGcFo5O4P9dMkLvMEQPXTg6gQcvG0mzVb5gCd8miVkIp5z4UBZPTiEsQBaq8EVLpqQyJjlcni8LnyeJWQinT/eU8vv3CqhubNU7FNEDj67ey1tbimWtbOHzZFS2EE6Hy+tZvvaQ3mGIHlp/6CRmPwOXjh2idyhC9Iq0mIVwOlxer3cIopeKKxtplUF8wsdJi1kIpx/Py2bG0Bje2HyUX186kv98U8TDH+xxHX/imnFMyYjilhfy+c2lIwm1mLj0ybVUNrjX9a0UvHzTZDJjg/n+P77hqcXjabbauPSJL10DlhaOineV3WK1s2RqKg+u2sWbW471yT33VEKYhdd/MI2ik4089tFe/nr1OFbvOsF9r293nfObS0dwwagE7lqxldtnZ5AZE8zlT31FsXMBF3c8tXg8eemR3Px8Pg9e5qjzS55cS5WzzqdmRPH4NY6yd5fU8MO5Q5GJUsLXSWIWwikiyMz83DimZEQSYjGxcFSCKzEHmo3MGRZLkL8fL9yYR4jFMUBsQmokH+0+4db140Mt5KVH4mc0sOK2Ka5r5CSEsrXIsUvSvOFxRAf7c+usDGx2DYvJyPzceK9LzFMzo0gICyAhLIC/L5lAiMXEeblx/OLNHVjtGgYF54+IJzrYnyevGee61xlZ0fwnv8itMoL9/ThnWAyBZj9evOm0Ok+J4OOCUgDmDo8lOtifqyYl09BiI8hfPtKE75OubCG+pS0BFFbUc83kFPwMiiVTUjlQVtfueH2zlWHxwaRHB3V5zSCzkcvGDaHwZEO7a5TVNjMtM4q4UH/mDY91tZxNRoNrdHF9i5XzR8R59iZ7ISc+hKTwAFqcsbbdy8Hyeq6bmoZBwXVT0zjofDTQdtxqsxMXZmF4Qtd7JpuMisWTUzhQ2v4adc1WhsWHkBYVyPSsKNdCIkopScpiwJCVv4Towvs7SlgwMqHD4x/vPsFNz3f+el52bhY/OW9Yh8fX7C0jLy2SAPPZp/o0tdqY8YdPKa9rdi/oPvTq7VOZlBbZ4fGu6mvzkUoWPfVVp2VcMzmF3y0a1eHx9QcrGBoXQmSQ7PwlfIOs/CWEB3WUZDRNo6nV5tZGF18fqKCp1dbhylSzs2M6TMotVju7S2qoqNc/KQN8ub+cplYbHX2p76q+vjrQdX1tOHSShhZrh6uxTc6IkqQsBixpMQvRQ40tNub+6TOOVbu3q5HJqHjvR7PIig3uVjm/ensn//zqcA8i7DuZMUG8/+NZmIzuf7e32TUW/uUL9pyodftvXrtjGhNSI3oSohBeRVrMQvSDALORzG4k2fBAM0kRAd0uZ9SQsG7/TV/LiQ/tVlIGMBoUOQkhbp8fZDaS4cbzeyEGGhktIUQP1TdbOW9EPNWNraRGBZGXHsnjH++jtNbR5Ww0KO5dkMPJhhb+800RP5w7lNKaZlKiArtVTojFj+umpvLZnjJumZXB53vLWL3LvZHgnpISGcgtszJYs6eMmsZWpmZG0WK1Y/ZzPzlbbXby0iMprWkm0GzknJxYnlt7iEOnzR+/a342RoPiyU/3c8+CHMrqmomQLmsxyEhXthC9VHC8hqSIQIL9/Xjm8wP87t0CwDEn+anFEwDYWHiSCakdD5jqis2usb24mrHJ4ZyoaWLK7z+mP9+6D10+iqvyUjhZ30J1Y6tbI9E7cqSigQCzkZgQf1ZuPMrdr24FYHJ6JP+5bSrQ+/oSwttIV7YQ/SgnPpRg51SdXcdqCAswYTEZKK5qoqHFCtDrJGM0KMYmhwPwzeGTJIZ1v0u8pyICTWwvrgYgMsjcq6QMkBIVSEyIPwDbj1YRGWTGZFRUN7Zysr4F6H19CeHLpMUshIdtLKxk29Eqbpie3mdlWG12zn/scw6U9e0yokaD4t0fzmRYvPvPhrtrf2kd/916jP+Zn91nZQiht+60mOUZsxAeNiE1os9HEhsNitB+2J7Sz6AItvTtx0RWbLAkZSFOI13ZQviYD3ce58pn1rH5SFWfl9VstXPd8vX88f2CPi9LCOEgiVkIH1FY4ei2HpcSwTeHT/ZbuQfK6klzPlduaLFSWuvevG0hRM9IV7YQXm5LURUbDlXw1pZj3HtBDh/sPN6vI7IBXvj6MAlhFj7YeZyT9S1cnZfC5PSobk2XEkK4RxKzEF5u85FK1xSsJcs36BLDjuKadmXXNlmZOTRGl1iEGOjk664QXq6uyap3CGeoa7Z2uFa2EKJ3JDEL4eUuHpPIY1eO1TsMl6vzknno8tFYO9iQQwjRO5KYhfByadFBXDImkVjnohx6u2pSCsPiQ7q9VrYQwj3yzhLCy31ScILfvLPLtQa33n7/3m7+880RWqxn35JRCNE7kpiF8HIVdS3848vDeofhsu7gST7bUyYjsoXoI/LOEsLLFRx3f//i/rK/tA6rTVrMQvQFmS4lhJf7yXnZLBgZz7NfHORP3xvLW1uKuf+NHa7jj189jjk5sdzx0kbuvSCH+FALFz2+lpJq9xcCeeXmyYxMCuP7z23gL1eNw2BQLHjsc2qdI8LPy43jz1eO5c3NxZyoaeK22ZkYlPL4vQohJDEL4fUCzX5MSotkZGIYAWYjs7NjUAo0DcxGAzOyoh1bTi6ZSIDZCMD41Aje2Vbi1vUjAk2MT43AYjLy8s1TXNfITQhl/SHHCmMzs2MI9vfjmrwUWmx2LCZj39ysEEK6soXwFW0J82hlI7fOzMDPoLh1VgaHnUt1th1varWRExfC8ITQrq9pMnL9tDQKKxraXaO6oZVpmdGkRgUyPzcOP4OjdWwwKEnKQvQx2fZRCB/1aUEpc3JiOzy+7mAFVz2zrtNr3Dorg58vHN7h8Q2HTjIiMZQgf+lcE6I3urPto7SYhfBRHSVlu12juqGVTwpKu7zG53vLqGpooanVdtbjeemRkpSF6GeSmIUYYGyaxuV/+5JnPj/Y5bkFx2uZ8OBHru5wIYT+JDELMcCYjAbSooLcPj/IbCQhLKAPIxJCdIf0UQkxwDS12lg4KoH6FiuxIRZmZcfw14/3ceSkY4CXQcEvLsqlqdXO3z8/wP0Lh1NZ30JYgEnnyIUQIIlZiAHHYjLynQlJTEiNIDLYTKjFRFOrjQfedMx9np0dww3T0wGYkRXNqKQwPcMVQnyLdGULMUClRQcRanG0gneX1BAd7I/FZKC6sZWaplYAScpCeCFpMQsxCPx20Sium5rG2v3l3DQjXe9whBCdkMQsxCAxLD6EYfEheochhOiCdGULIYQQXkQSsxBCCOFFJDELIYQQXkQSsxBCCOFFep2YlVLLlFJ7lFI7lVJ/9ERQQgghxGDVq1HZSqk5wKXAaE3TmpVSHW91I4QQQogu9bbFfAfwkKZpzQCapnW9nY0QQgghOtTbxJwNzFRKrVdKrVFKTeroRKXUrUqpfKVUfllZWS+LFUIIIQamLruylVIfAfFnOXS/8+8jgCnAJGCFUipD0zTt2ydrmvYM8AzAxIkTzzguhBBCCDcSs6Zp8zo6ppS6A3jdmYg3KKXsQDQgTWIhhBCiB3rblf0mcC6AUiobMAPlvQ1KCCGEGKx6u1b2c8BzSqkdQAtw/dm6sYUQQgjhnl4lZk3TWoBrPRSLEEIIMejJyl9CCCGEF5HELIQQQngRScxCCCGEF5HELIQQQngRScxCCCGEF5HELIQQQngRpce0Y6VUGVDogUtFIwuanE7q40xSJ+1JfZxJ6qQ9qY8zeaJOUjVNi3HnRF0Ss6copfI1TZuodxzeQurjTFIn7Ul9nEnqpD2pjzP1d51IV7YQQgjhRSQxCyGEEF7E1xPzM3oH4GWkPs4kddKe1MeZpE7ak/o4U7/WiU8/YxZCCCEGGl9vMQshhBADiiRmIYQQwov4fGJWSi1TSu1RSu1USv1R73i8hVLqbqWUppSK1jsWPSmlHlZKFSiltiml3lBKhesdk16UUguc75X9Sql79Y5HT0qpZKXUp0qp3c7Pjh/pHZM3UEoZlVKblVKr9I7FGyilwpVSK52fIbuVUlP7o1yfTsxKqTnApcBoTdNGAI/oHJJXUEolA/OBI3rH4gVWAyM1TRsN7AXu0zkeXSiljMCTwAVALnC1UipX36h0ZQV+omnacGAKcOcgr482PwJ26x2EF/kL8L6maTnAGPqpbnw6MQN3AA9pmtYMoGlaqc7xeItHgXuAQT+yT9O0DzVNszp/XAck6RmPjvKA/ZqmHdQ0rQX4N44vtYOSpmklmqZtcv5/LY4P3CH6RqUvpVQScCHwrN6xeAOlVCgwC1gOoGlai6ZpVf1Rtq8n5mxgplJqvVJqjVJqkt4B6U0pdQlQrGnaVr1j8UI3Au/pHYROhgBFp/18lEGeiNoopdKAccB6fSPR3WM4vtDb9Q7ES2QAZcA/nN37zyqlgvqjYL/+KKQ3lFIfAfFnOXQ/jvgjcHRFTQJWKKUytAE+B6yLOvk5cF7/RqSvzupD07S3nOfcj6P78uX+jM2LqLP8bkC/T9yhlAoGXgN+rGlajd7x6EUpdRFQqmnaRqXUOXrH4yX8gPHAMk3T1iul/gLcC/yiPwr2apqmzevomFLqDuB1ZyLeoJSy41hsvKy/4tNDR3WilBoFpANblVLg6LbdpJTK0zTteD+G2K86e40AKKWuBy4C5g70L22dOAokn/ZzEnBMp1i8glLKhCMpv6xp2ut6x6Oz6cAlSqmFgAUIVUq9pGnatTrHpaejwFFN09p6UlbiSMx9zte7st8EzgVQSmUDZgbxriiapm3XNC1W07Q0TdPScLywxg/kpNwVpdQC4GfAJZqmNegdj46+AYYqpdKVUmbgKuBtnWPSjXJ8c10O7NY07c96x6M3TdPu0zQtyfm5cRXwySBPyjg/N4uUUsOcv5oL7OqPsr2+xdyF54DnlFI7gBbg+kHcIhJn9wTgD6x29iKs0zTtdn1D6n+aplmVUkuBDwAj8JymaTt1DktP04ElwHal1Bbn736uadq7OsYkvM8y4GXnl9mDwA39UagsySmEEEJ4EV/vyhZCCCEGFEnMQgghhBeRxCyEEEJ4EUnMQgghhBeRxCyEEEJ4EUnMQgghhBeRxCyEEEJ4kf8PP9nOxxxmZbIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x, y = koch_snowflake(order=5)\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.axis('equal')\n",
    "plt.fill(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Date handling\n",
    "=============\n",
    "\n",
    "You can plot timeseries data with major and minor ticks and custom\n",
    "tick formatters for both.\n",
    "\n",
    ".. figure:: ../../gallery/text_labels_and_annotations/images/sphx_glr_date_001.png\n",
    "   :target: ../../gallery/text_labels_and_annotations/date.html\n",
    "   :align: center\n",
    "   :scale: 50\n",
    "\n",
    "   Date\n",
    "\n",
    "See :mod:`matplotlib.ticker` and :mod:`matplotlib.dates` for details and usage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'D:\\\\pcApp\\\\Anaconda3\\\\lib\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\sample_data\\\\goog.npz'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-28-054588e080dc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[1;31m# stores the date as an np.datetime64 with a day unit ('D') in the 'date'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;31m# column.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 14\u001b[1;33m \u001b[1;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_sample_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'goog.npz'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mdatafile\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     15\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdatafile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'price_data'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\pcApp\\Anaconda3\\lib\\site-packages\\matplotlib\\cbook\\__init__.py\u001b[0m in \u001b[0;36mget_sample_data\u001b[1;34m(fname, asfileobj)\u001b[0m\n\u001b[0;32m    680\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mgzip\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    681\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 682\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    683\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    684\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'D:\\\\pcApp\\\\Anaconda3\\\\lib\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\sample_data\\\\goog.npz'"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "import matplotlib.cbook as cbook\n",
    "\n",
    "years = mdates.YearLocator()   # every year\n",
    "months = mdates.MonthLocator()  # every month\n",
    "years_fmt = mdates.DateFormatter('%Y')\n",
    "\n",
    "# Load a numpy structured array from yahoo csv data with fields date, open,\n",
    "# close, volume, adj_close from the mpl-data/example directory.  This array\n",
    "# stores the date as an np.datetime64 with a day unit ('D') in the 'date'\n",
    "# column.\n",
    "with cbook.get_sample_data('goog.npz') as datafile:\n",
    "    data = np.load(datafile)['price_data']\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot('date', 'adj_close', data=data)\n",
    "\n",
    "# format the ticks\n",
    "ax.xaxis.set_major_locator(years)\n",
    "ax.xaxis.set_major_formatter(years_fmt)\n",
    "ax.xaxis.set_minor_locator(months)\n",
    "\n",
    "# round to nearest years.\n",
    "datemin = np.datetime64(data['date'][0], 'Y')\n",
    "datemax = np.datetime64(data['date'][-1], 'Y') + np.timedelta64(1, 'Y')\n",
    "ax.set_xlim(datemin, datemax)\n",
    "\n",
    "# format the coords message box\n",
    "ax.format_xdata = mdates.DateFormatter('%Y-%m-%d')\n",
    "ax.format_ydata = lambda x: '$%1.2f' % x  # format the price.\n",
    "ax.grid(True)\n",
    "\n",
    "# rotates and right aligns the x labels, and moves the bottom of the\n",
    "# axes up to make room for them\n",
    "fig.autofmt_xdate()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Log plots\n",
    "=========\n",
    "\n",
    "The :func:`~matplotlib.pyplot.semilogx`,\n",
    ":func:`~matplotlib.pyplot.semilogy` and\n",
    ":func:`~matplotlib.pyplot.loglog` functions simplify the creation of\n",
    "logarithmic plots.\n",
    "\n",
    ".. figure:: ../../gallery/scales/images/sphx_glr_log_demo_001.png\n",
    "   :target: ../../gallery/scales/log_demo.html\n",
    "   :align: center\n",
    "   :scale: 50\n",
    "\n",
    "   Log Demo\n",
    "\n",
    "Thanks to Andrew Straw, Darren Dale and Gregory Lielens for contributions\n",
    "log-scaling infrastructure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Data for plotting\n",
    "t = np.arange(0.01, 20.0, 0.01)\n",
    "\n",
    "# Create figure\n",
    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)\n",
    "\n",
    "# log y axis\n",
    "ax1.semilogy(t, np.exp(-t / 5.0))\n",
    "ax1.set(title='semilogy')\n",
    "ax1.grid()\n",
    "\n",
    "# log x axis\n",
    "ax2.semilogx(t, np.sin(2 * np.pi * t))\n",
    "ax2.set(title='semilogx')\n",
    "ax2.grid()\n",
    "\n",
    "# log x and y axis\n",
    "ax3.loglog(t, 20 * np.exp(-t / 10.0), basex=2)\n",
    "ax3.set(title='loglog base 2 on x')\n",
    "ax3.grid()\n",
    "\n",
    "# With errorbars: clip non-positive values\n",
    "# Use new data for plotting\n",
    "x = 10.0**np.linspace(0.0, 2.0, 20)\n",
    "y = x**2.0\n",
    "\n",
    "ax4.set_xscale(\"log\", nonposx='clip')\n",
    "ax4.set_yscale(\"log\", nonposy='clip')\n",
    "ax4.set(title='Errorbars go negative')\n",
    "ax4.errorbar(x, y, xerr=0.1 * x, yerr=5.0 + 0.75 * y)\n",
    "# ylim must be set after errorbar to allow errorbar to autoscale limits\n",
    "ax4.set_ylim(bottom=0.1)\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Polar plots\n",
    "===========\n",
    "\n",
    "The :func:`~matplotlib.pyplot.polar` function generates polar plots.\n",
    "\n",
    ".. figure:: ../../gallery/pie_and_polar_charts/images/sphx_glr_polar_demo_001.png\n",
    "   :target: ../../gallery/pie_and_polar_charts/polar_demo.html\n",
    "   :align: center\n",
    "   :scale: 50\n",
    "\n",
    "   Polar Demo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "r = np.arange(0, 2, 0.01)\n",
    "theta = 2 * np.pi * r\n",
    "\n",
    "ax = plt.subplot(111, projection='polar')\n",
    "ax.plot(theta, r)\n",
    "ax.set_rmax(2)\n",
    "ax.set_rticks([0.5, 1, 1.5, 2])  # Less radial ticks\n",
    "ax.set_rlabel_position(-22.5)  # Move radial labels away from plotted line\n",
    "ax.grid(True)\n",
    "\n",
    "ax.set_title(\"A line plot on a polar axis\", va='bottom')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Legends\n",
    "=======\n",
    "\n",
    "The :func:`~matplotlib.pyplot.legend` function automatically\n",
    "generates figure legends, with MATLAB-compatible legend-placement\n",
    "functions.\n",
    "\n",
    ".. figure:: ../../gallery/text_labels_and_annotations/images/sphx_glr_legend_001.png\n",
    "   :target: ../../gallery/text_labels_and_annotations/legend.html\n",
    "   :align: center\n",
    "   :scale: 50\n",
    "\n",
    "   Legend\n",
    "\n",
    "Thanks to Charles Twardy for input on the legend function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Make some fake data.\n",
    "a = b = np.arange(0, 3, .02)\n",
    "c = np.exp(a)\n",
    "d = c[::-1]\n",
    "\n",
    "# Create plots with pre-defined labels.\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(a, c, 'k--', label='Model length')\n",
    "ax.plot(a, d, 'k:', label='Data length')\n",
    "ax.plot(a, c + d, 'k', label='Total message length')\n",
    "\n",
    "legend = ax.legend(loc='upper center', shadow=True, fontsize='x-large')\n",
    "\n",
    "# Put a nicer background color on the legend.\n",
    "legend.get_frame().set_facecolor('C0')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TeX-notation for text objects\n",
    "=============================\n",
    "\n",
    "Below is a sampling of the many TeX expressions now supported by Matplotlib's\n",
    "internal mathtext engine.  The mathtext module provides TeX style mathematical\n",
    "expressions using `FreeType <https://www.freetype.org/>`_\n",
    "and the DejaVu, BaKoMa computer modern, or `STIX <http://www.stixfonts.org>`_\n",
    "fonts.  See the :mod:`matplotlib.mathtext` module for additional details.\n",
    "\n",
    ".. figure:: ../../gallery/text_labels_and_annotations/images/sphx_glr_mathtext_examples_001.png\n",
    "   :target: ../../gallery/text_labels_and_annotations/mathtext_examples.html\n",
    "   :align: center\n",
    "   :scale: 50\n",
    "\n",
    "   Mathtext Examples\n",
    "\n",
    "Matplotlib's mathtext infrastructure is an independent implementation and\n",
    "does not require TeX or any external packages installed on your computer. See\n",
    "the tutorial at :doc:`/tutorials/text/mathtext`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 $W^{3\\beta}_{\\delta_1 \\rho_1 \\sigma_2} = U^{3\\beta}_{\\delta_1 \\rho_1} + \\frac{1}{8 \\pi 2} \\int^{\\alpha_2}_{\\alpha_2} d \\alpha^\\prime_2 \\left[\\frac{ U^{2\\beta}_{\\delta_1 \\rho_1} - \\alpha^\\prime_2U^{1\\beta}_{\\rho_1 \\sigma_2} }{U^{0\\beta}_{\\rho_1 \\sigma_2}}\\right]$\n",
      "1 $\\alpha_i > \\beta_i,\\ \\alpha_{i+1}^j = {\\rm sin}(2\\pi f_j t_i) e^{-5 t_i/\\tau},\\ \\ldots$\n",
      "2 $\\frac{3}{4},\\ \\binom{3}{4},\\ \\genfrac{}{}{0}{}{3}{4},\\ \\left(\\frac{5 - \\frac{1}{x}}{4}\\right),\\ \\ldots$\n",
      "3 $\\sqrt{2},\\ \\sqrt[3]{x},\\ \\ldots$\n",
      "4 $\\mathrm{Roman}\\ , \\ \\mathit{Italic}\\ , \\ \\mathtt{Typewriter} \\ \\mathrm{or}\\ \\mathcal{CALLIGRAPHY}$\n",
      "5 $\\acute a,\\ \\bar a,\\ \\breve a,\\ \\dot a,\\ \\ddot a, \\ \\grave a, \\ \\hat a,\\ \\tilde a,\\ \\vec a,\\ \\widehat{xyz},\\ \\widetilde{xyz},\\ \\ldots$\n",
      "6 $\\alpha,\\ \\beta,\\ \\chi,\\ \\delta,\\ \\lambda,\\ \\mu,\\ \\Delta,\\ \\Gamma,\\ \\Omega,\\ \\Phi,\\ \\Pi,\\ \\Upsilon,\\ \\nabla,\\ \\aleph,\\ \\beth,\\ \\daleth,\\ \\gimel,\\ \\ldots$\n",
      "7 $\\coprod,\\ \\int,\\ \\oint,\\ \\prod,\\ \\sum,\\ \\log,\\ \\sin,\\ \\approx,\\ \\oplus,\\ \\star,\\ \\varpropto,\\ \\infty,\\ \\partial,\\ \\Re,\\ \\leftrightsquigarrow, \\ \\ldots$\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import subprocess\n",
    "import sys\n",
    "import re\n",
    "\n",
    "# Selection of features following \"Writing mathematical expressions\" tutorial\n",
    "mathtext_titles = {\n",
    "    0: \"Header demo\",\n",
    "    1: \"Subscripts and superscripts\",\n",
    "    2: \"Fractions, binomials and stacked numbers\",\n",
    "    3: \"Radicals\",\n",
    "    4: \"Fonts\",\n",
    "    5: \"Accents\",\n",
    "    6: \"Greek, Hebrew\",\n",
    "    7: \"Delimiters, functions and Symbols\"}\n",
    "n_lines = len(mathtext_titles)\n",
    "\n",
    "# Randomly picked examples\n",
    "mathext_demos = {\n",
    "    0: r\"$W^{3\\beta}_{\\delta_1 \\rho_1 \\sigma_2} = \"\n",
    "    r\"U^{3\\beta}_{\\delta_1 \\rho_1} + \\frac{1}{8 \\pi 2} \"\n",
    "    r\"\\int^{\\alpha_2}_{\\alpha_2} d \\alpha^\\prime_2 \\left[\\frac{ \"\n",
    "    r\"U^{2\\beta}_{\\delta_1 \\rho_1} - \\alpha^\\prime_2U^{1\\beta}_\"\n",
    "    r\"{\\rho_1 \\sigma_2} }{U^{0\\beta}_{\\rho_1 \\sigma_2}}\\right]$\",\n",
    "\n",
    "    1: r\"$\\alpha_i > \\beta_i,\\ \"\n",
    "    r\"\\alpha_{i+1}^j = {\\rm sin}(2\\pi f_j t_i) e^{-5 t_i/\\tau},\\ \"\n",
    "    r\"\\ldots$\",\n",
    "\n",
    "    2: r\"$\\frac{3}{4},\\ \\binom{3}{4},\\ \\genfrac{}{}{0}{}{3}{4},\\ \"\n",
    "    r\"\\left(\\frac{5 - \\frac{1}{x}}{4}\\right),\\ \\ldots$\",\n",
    "\n",
    "    3: r\"$\\sqrt{2},\\ \\sqrt[3]{x},\\ \\ldots$\",\n",
    "\n",
    "    4: r\"$\\mathrm{Roman}\\ , \\ \\mathit{Italic}\\ , \\ \\mathtt{Typewriter} \\ \"\n",
    "    r\"\\mathrm{or}\\ \\mathcal{CALLIGRAPHY}$\",\n",
    "\n",
    "    5: r\"$\\acute a,\\ \\bar a,\\ \\breve a,\\ \\dot a,\\ \\ddot a, \\ \\grave a, \\ \"\n",
    "    r\"\\hat a,\\ \\tilde a,\\ \\vec a,\\ \\widehat{xyz},\\ \\widetilde{xyz},\\ \"\n",
    "    r\"\\ldots$\",\n",
    "\n",
    "    6: r\"$\\alpha,\\ \\beta,\\ \\chi,\\ \\delta,\\ \\lambda,\\ \\mu,\\ \"\n",
    "    r\"\\Delta,\\ \\Gamma,\\ \\Omega,\\ \\Phi,\\ \\Pi,\\ \\Upsilon,\\ \\nabla,\\ \"\n",
    "    r\"\\aleph,\\ \\beth,\\ \\daleth,\\ \\gimel,\\ \\ldots$\",\n",
    "\n",
    "    7: r\"$\\coprod,\\ \\int,\\ \\oint,\\ \\prod,\\ \\sum,\\ \"\n",
    "    r\"\\log,\\ \\sin,\\ \\approx,\\ \\oplus,\\ \\star,\\ \\varpropto,\\ \"\n",
    "    r\"\\infty,\\ \\partial,\\ \\Re,\\ \\leftrightsquigarrow, \\ \\ldots$\"}\n",
    "\n",
    "\n",
    "def doall():\n",
    "    # Colors used in mpl online documentation.\n",
    "    mpl_blue_rvb = (191. / 255., 209. / 256., 212. / 255.)\n",
    "    mpl_orange_rvb = (202. / 255., 121. / 256., 0. / 255.)\n",
    "    mpl_grey_rvb = (51. / 255., 51. / 255., 51. / 255.)\n",
    "\n",
    "    # Creating figure and axis.\n",
    "    plt.figure(figsize=(6, 7))\n",
    "    plt.axes([0.01, 0.01, 0.98, 0.90], facecolor=\"white\", frameon=True)\n",
    "    plt.gca().set_xlim(0., 1.)\n",
    "    plt.gca().set_ylim(0., 1.)\n",
    "    plt.gca().set_title(\"Matplotlib's math rendering engine\",\n",
    "                        color=mpl_grey_rvb, fontsize=14, weight='bold')\n",
    "    plt.gca().set_xticklabels(\"\", visible=False)\n",
    "    plt.gca().set_yticklabels(\"\", visible=False)\n",
    "\n",
    "    # Gap between lines in axes coords\n",
    "    line_axesfrac = (1. / (n_lines))\n",
    "\n",
    "    # Plotting header demonstration formula\n",
    "    full_demo = mathext_demos[0]\n",
    "    plt.annotate(full_demo,\n",
    "                 xy=(0.5, 1. - 0.59 * line_axesfrac),\n",
    "                 color=mpl_orange_rvb, ha='center', fontsize=20)\n",
    "\n",
    "    # Plotting features demonstration formulae\n",
    "    for i_line in range(1, n_lines):\n",
    "        baseline = 1 - (i_line) * line_axesfrac\n",
    "        baseline_next = baseline - line_axesfrac\n",
    "        title = mathtext_titles[i_line] + \":\"\n",
    "        fill_color = ['white', mpl_blue_rvb][i_line % 2]\n",
    "        plt.fill_between([0., 1.], [baseline, baseline],\n",
    "                         [baseline_next, baseline_next],\n",
    "                         color=fill_color, alpha=0.5)\n",
    "        plt.annotate(title,\n",
    "                     xy=(0.07, baseline - 0.3 * line_axesfrac),\n",
    "                     color=mpl_grey_rvb, weight='bold')\n",
    "        demo = mathext_demos[i_line]\n",
    "        plt.annotate(demo,\n",
    "                     xy=(0.05, baseline - 0.75 * line_axesfrac),\n",
    "                     color=mpl_grey_rvb, fontsize=16)\n",
    "\n",
    "    for i in range(n_lines):\n",
    "        s = mathext_demos[i]\n",
    "        print(i, s)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "if '--latex' in sys.argv:\n",
    "    # Run: python mathtext_examples.py --latex\n",
    "    # Need amsmath and amssymb packages.\n",
    "    fd = open(\"mathtext_examples.ltx\", \"w\")\n",
    "    fd.write(\"\\\\documentclass{article}\\n\")\n",
    "    fd.write(\"\\\\usepackage{amsmath, amssymb}\\n\")\n",
    "    fd.write(\"\\\\begin{document}\\n\")\n",
    "    fd.write(\"\\\\begin{enumerate}\\n\")\n",
    "\n",
    "    for i in range(n_lines):\n",
    "        s = mathext_demos[i]\n",
    "        s = re.sub(r\"(?<!\\\\)\\$\", \"$$\", s)\n",
    "        fd.write(\"\\\\item %s\\n\" % s)\n",
    "\n",
    "    fd.write(\"\\\\end{enumerate}\\n\")\n",
    "    fd.write(\"\\\\end{document}\\n\")\n",
    "    fd.close()\n",
    "\n",
    "    subprocess.call([\"pdflatex\", \"mathtext_examples.ltx\"])\n",
    "else:\n",
    "    doall()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Native TeX rendering\n",
    "====================\n",
    "\n",
    "Although Matplotlib's internal math rendering engine is quite\n",
    "powerful, sometimes you need TeX. Matplotlib supports external TeX\n",
    "rendering of strings with the *usetex* option.\n",
    "\n",
    ".. figure:: ../../gallery/text_labels_and_annotations/images/sphx_glr_tex_demo_001.png\n",
    "   :target: ../../gallery/text_labels_and_annotations/tex_demo.html\n",
    "   :align: center\n",
    "   :scale: 50\n",
    "\n",
    "   Tex Demo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[WinError 2] 系统找不到指定的文件。",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32mD:\\pcApp\\Anaconda3\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    339\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    340\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    342\u001b[0m             \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\pcApp\\Anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m    239\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    240\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 241\u001b[1;33m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'png'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    242\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'retina'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m'png2x'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    243\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\pcApp\\Anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m    123\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    124\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 125\u001b[1;33m     \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    126\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    127\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'svg'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\pcApp\\Anaconda3\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[0;32m   2210\u001b[0m                     \u001b[0morientation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2211\u001b[0m                     \u001b[0mdryrun\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2212\u001b[1;33m                     **kwargs)\n\u001b[0m\u001b[0;32m   2213\u001b[0m                 \u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2214\u001b[0m                 \u001b[0mbbox_inches\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\pcApp\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[1;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[0;32m    511\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    512\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mprint_png\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilename_or_obj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 513\u001b[1;33m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    514\u001b[0m         \u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    515\u001b[0m         \u001b[0moriginal_dpi\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\pcApp\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    431\u001b[0m             \u001b[1;31m# if toolbar:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    432\u001b[0m             \u001b[1;31m#     toolbar.set_cursor(cursors.WAIT)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 433\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    434\u001b[0m             \u001b[1;31m# A GUI class may be need to update a window using this draw, so\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    435\u001b[0m             \u001b[1;31m# don't forget to call the superclass.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\pcApp\\Anaconda3\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     54\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 55\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     56\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     57\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\pcApp\\Anaconda3\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m   1464\u001b[0m                 \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1465\u001b[0m                     self.tight_layout(renderer,\n\u001b[1;32m-> 1466\u001b[1;33m                                       **self._tight_parameters)\n\u001b[0m\u001b[0;32m   1467\u001b[0m                 \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1468\u001b[0m                     \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\pcApp\\Anaconda3\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mtight_layout\u001b[1;34m(self, renderer, pad, h_pad, w_pad, rect)\u001b[0m\n\u001b[0;32m   2273\u001b[0m         kwargs = get_tight_layout_figure(\n\u001b[0;32m   2274\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msubplotspec_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2275\u001b[1;33m             pad=pad, h_pad=h_pad, w_pad=w_pad, rect=rect)\n\u001b[0m\u001b[0;32m   2276\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplots_adjust\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2277\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\pcApp\\Anaconda3\\lib\\site-packages\\matplotlib\\tight_layout.py\u001b[0m in \u001b[0;36mget_tight_layout_figure\u001b[1;34m(fig, axes_list, subplotspec_list, renderer, pad, h_pad, w_pad, rect)\u001b[0m\n\u001b[0;32m    326\u001b[0m                                      \u001b[0msubplot_list\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msubplot_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    327\u001b[0m                                      \u001b[0max_bbox_list\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0max_bbox_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 328\u001b[1;33m                                      pad=pad, h_pad=h_pad, w_pad=w_pad)\n\u001b[0m\u001b[0;32m    329\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    330\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mrect\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\pcApp\\Anaconda3\\lib\\site-packages\\matplotlib\\tight_layout.py\u001b[0m in \u001b[0;36mauto_adjust_subplotpars\u001b[1;34m(fig, renderer, nrows_ncols, num1num2_list, subplot_list, ax_bbox_list, pad, h_pad, w_pad, rect)\u001b[0m\n\u001b[0;32m    112\u001b[0m             \u001b[1;32mcontinue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    113\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 114\u001b[1;33m         tight_bbox_raw = union([ax.get_tightbbox(renderer) for ax in subplots\n\u001b[0m\u001b[0;32m    115\u001b[0m                                 if ax.get_visible()])\n\u001b[0;32m    116\u001b[0m         tight_bbox = TransformedBbox(tight_bbox_raw,\n",
      "\u001b[1;32mD:\\pcApp\\Anaconda3\\lib\\site-packages\\matplotlib\\tight_layout.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    113\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    114\u001b[0m         tight_bbox_raw = union([ax.get_tightbbox(renderer) for ax in subplots\n\u001b[1;32m--> 115\u001b[1;33m                                 if ax.get_visible()])\n\u001b[0m\u001b[0;32m    116\u001b[0m         tight_bbox = TransformedBbox(tight_bbox_raw,\n\u001b[0;32m    117\u001b[0m                                      fig.transFigure.inverted())\n",
      "\u001b[1;32mD:\\pcApp\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36mget_tightbbox\u001b[1;34m(self, renderer, call_axes_locator)\u001b[0m\n\u001b[0;32m   4162\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4163\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_visible\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4164\u001b[1;33m             \u001b[0mbb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_window_extent\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4165\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_left_title\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_visible\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4166\u001b[0m             \u001b[0mbb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_left_title\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_window_extent\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\pcApp\\Anaconda3\\lib\\site-packages\\matplotlib\\text.py\u001b[0m in \u001b[0;36mget_window_extent\u001b[1;34m(self, renderer, dpi)\u001b[0m\n\u001b[0;32m    920\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Cannot get window extent w/o renderer'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    921\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 922\u001b[1;33m         \u001b[0mbbox\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minfo\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdescent\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_layout\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_renderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    923\u001b[0m         \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_unitless_position\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    924\u001b[0m         \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransform_point\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\pcApp\\Anaconda3\\lib\\site-packages\\matplotlib\\text.py\u001b[0m in \u001b[0;36m_get_layout\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m    307\u001b[0m                 w, h, d = renderer.get_text_width_height_descent(clean_line,\n\u001b[0;32m    308\u001b[0m                                                         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fontproperties\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 309\u001b[1;33m                                                         ismath=ismath)\n\u001b[0m\u001b[0;32m    310\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    311\u001b[0m                 \u001b[0mw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0md\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\pcApp\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mget_text_width_height_descent\u001b[1;34m(self, s, prop, ismath)\u001b[0m\n\u001b[0;32m    230\u001b[0m             \u001b[0mfontsize\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mprop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_size_in_points\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    231\u001b[0m             w, h, d = texmanager.get_text_width_height_descent(\n\u001b[1;32m--> 232\u001b[1;33m                 s, fontsize, renderer=self)\n\u001b[0m\u001b[0;32m    233\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0md\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    234\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\pcApp\\Anaconda3\\lib\\site-packages\\matplotlib\\texmanager.py\u001b[0m in \u001b[0;36mget_text_width_height_descent\u001b[1;34m(self, tex, fontsize, renderer)\u001b[0m\n\u001b[0;32m    499\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    500\u001b[0m             \u001b[1;31m# use dviread. It sometimes returns a wrong descent.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 501\u001b[1;33m             \u001b[0mdvifile\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmake_dvi\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    502\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mdviread\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDvi\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdvifile\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m72\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mdpi_fraction\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mdvi\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    503\u001b[0m                 \u001b[0mpage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdvi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\pcApp\\Anaconda3\\lib\\site-packages\\matplotlib\\texmanager.py\u001b[0m in \u001b[0;36mmake_dvi\u001b[1;34m(self, tex, fontsize)\u001b[0m\n\u001b[0;32m    363\u001b[0m                 self._run_checked_subprocess(\n\u001b[0;32m    364\u001b[0m                     [\"latex\", \"-interaction=nonstopmode\", \"--halt-on-error\",\n\u001b[1;32m--> 365\u001b[1;33m                      texfile], tex)\n\u001b[0m\u001b[0;32m    366\u001b[0m             \u001b[1;32mfor\u001b[0m \u001b[0mfname\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mglob\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mglob\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbasefile\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m'*'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    367\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mfname\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mendswith\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'dvi'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'tex'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\pcApp\\Anaconda3\\lib\\site-packages\\matplotlib\\texmanager.py\u001b[0m in \u001b[0;36m_run_checked_subprocess\u001b[1;34m(self, command, tex)\u001b[0m\n\u001b[0;32m    333\u001b[0m             report = subprocess.check_output(command,\n\u001b[0;32m    334\u001b[0m                                              \u001b[0mcwd\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtexcache\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 335\u001b[1;33m                                              stderr=subprocess.STDOUT)\n\u001b[0m\u001b[0;32m    336\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0msubprocess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCalledProcessError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    337\u001b[0m             raise RuntimeError(\n",
      "\u001b[1;32mD:\\pcApp\\Anaconda3\\lib\\subprocess.py\u001b[0m in \u001b[0;36mcheck_output\u001b[1;34m(timeout, *popenargs, **kwargs)\u001b[0m\n\u001b[0;32m    334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    335\u001b[0m     return run(*popenargs, stdout=PIPE, timeout=timeout, check=True,\n\u001b[1;32m--> 336\u001b[1;33m                **kwargs).stdout\n\u001b[0m\u001b[0;32m    337\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    338\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\pcApp\\Anaconda3\\lib\\subprocess.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(input, timeout, check, *popenargs, **kwargs)\u001b[0m\n\u001b[0;32m    401\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'stdin'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mPIPE\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    402\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 403\u001b[1;33m     \u001b[1;32mwith\u001b[0m \u001b[0mPopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mpopenargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mprocess\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    404\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    405\u001b[0m             \u001b[0mstdout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstderr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mprocess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcommunicate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\pcApp\\Anaconda3\\lib\\subprocess.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, args, bufsize, executable, stdin, stdout, stderr, preexec_fn, close_fds, shell, cwd, env, universal_newlines, startupinfo, creationflags, restore_signals, start_new_session, pass_fds, encoding, errors)\u001b[0m\n\u001b[0;32m    707\u001b[0m                                 \u001b[0mc2pread\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mc2pwrite\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    708\u001b[0m                                 \u001b[0merrread\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrwrite\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 709\u001b[1;33m                                 restore_signals, start_new_session)\n\u001b[0m\u001b[0;32m    710\u001b[0m         \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    711\u001b[0m             \u001b[1;31m# Cleanup if the child failed starting.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\pcApp\\Anaconda3\\lib\\subprocess.py\u001b[0m in \u001b[0;36m_execute_child\u001b[1;34m(self, args, executable, preexec_fn, close_fds, pass_fds, cwd, env, startupinfo, creationflags, shell, p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite, unused_restore_signals, unused_start_new_session)\u001b[0m\n\u001b[0;32m    995\u001b[0m                                          \u001b[0menv\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    996\u001b[0m                                          \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfspath\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcwd\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcwd\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 997\u001b[1;33m                                          startupinfo)\n\u001b[0m\u001b[0;32m    998\u001b[0m             \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    999\u001b[0m                 \u001b[1;31m# Child is launched. Close the parent's copy of those pipe\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [WinError 2] 系统找不到指定的文件。"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib\n",
    "matplotlib.rcParams['text.usetex'] = True\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "t = np.linspace(0.0, 1.0, 100)\n",
    "s = np.cos(4 * np.pi * t) + 2\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 4), tight_layout=True)\n",
    "ax.plot(t, s)\n",
    "\n",
    "ax.set_xlabel(r'\\textbf{time (s)}')\n",
    "ax.set_ylabel('\\\\textit{Velocity (\\N{DEGREE SIGN}/sec)}', fontsize=16)\n",
    "ax.set_title(r'\\TeX\\ is Number $\\displaystyle\\sum_{n=1}^\\infty'\n",
    "             r'\\frac{-e^{i\\pi}}{2^n}$!', fontsize=16, color='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "EEG GUI\n",
    "=======\n",
    "\n",
    "You can embed Matplotlib into pygtk, wx, Tk, or Qt applications.\n",
    "Here is a screenshot of an EEG viewer called `pbrain\n",
    "<https://github.com/nipy/pbrain>`__.\n",
    "\n",
    "![](../../_static/eeg_small.png)\n",
    "\n",
    "\n",
    "The lower axes uses :func:`~matplotlib.pyplot.specgram`\n",
    "to plot the spectrogram of one of the EEG channels.\n",
    "\n",
    "For examples of how to embed Matplotlib in different toolkits, see:\n",
    "\n",
    "   * :doc:`/gallery/user_interfaces/embedding_in_gtk3_sgskip`\n",
    "   * :doc:`/gallery/user_interfaces/embedding_in_wx2_sgskip`\n",
    "   * :doc:`/gallery/user_interfaces/mpl_with_glade3_sgskip`\n",
    "   * :doc:`/gallery/user_interfaces/embedding_in_qt_sgskip`\n",
    "   * :doc:`/gallery/user_interfaces/embedding_in_tk_sgskip`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "XKCD-style sketch plots\n",
    "=======================\n",
    "\n",
    "Just for fun, Matplotlib supports plotting in the style of `xkcd\n",
    "<http://www.xkcd.com/>`.\n",
    "\n",
    ".. figure:: ../../gallery/showcase/images/sphx_glr_xkcd_001.png\n",
    "   :target: ../../gallery/showcase/xkcd.html\n",
    "   :align: center\n",
    "   :scale: 50\n",
    "\n",
    "   xkcd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "xkcd mode is not compatible with text.usetex = True",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-34-8d3214cfa1c8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[1;32mwith\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxkcd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m     \u001b[1;31m# Based on \"Stove Ownership\" from XKCD by Randall Munroe\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[1;31m# https://xkcd.com/418/\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\pcApp\\Anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mxkcd\u001b[1;34m(scale, length, randomness)\u001b[0m\n\u001b[0;32m    390\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mrcParams\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'text.usetex'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    391\u001b[0m         raise RuntimeError(\n\u001b[1;32m--> 392\u001b[1;33m             \"xkcd mode is not compatible with text.usetex = True\")\n\u001b[0m\u001b[0;32m    393\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    394\u001b[0m     \u001b[1;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpatheffects\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mRuntimeError\u001b[0m: xkcd mode is not compatible with text.usetex = True"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "with plt.xkcd():\n",
    "    # Based on \"Stove Ownership\" from XKCD by Randall Munroe\n",
    "    # https://xkcd.com/418/\n",
    "\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_axes((0.1, 0.2, 0.8, 0.7))\n",
    "    ax.spines['right'].set_color('none')\n",
    "    ax.spines['top'].set_color('none')\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])\n",
    "    ax.set_ylim([-30, 10])\n",
    "\n",
    "    data = np.ones(100)\n",
    "    data[70:] -= np.arange(30)\n",
    "\n",
    "    ax.annotate(\n",
    "        'THE DAY I REALIZED\\nI COULD COOK BACON\\nWHENEVER I WANTED',\n",
    "        xy=(70, 1), arrowprops=dict(arrowstyle='->'), xytext=(15, -10))\n",
    "\n",
    "    ax.plot(data)\n",
    "\n",
    "    ax.set_xlabel('time')\n",
    "    ax.set_ylabel('my overall health')\n",
    "    fig.text(\n",
    "        0.5, 0.05,\n",
    "        '\"Stove Ownership\" from xkcd by Randall Munroe',\n",
    "        ha='center')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Subplot example\n",
    "===============\n",
    "\n",
    "Many plot types can be combined in one figure to create\n",
    "powerful and flexible representations of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "np.random.seed(19680801)\n",
    "data = np.random.randn(2, 100)\n",
    "\n",
    "fig, axs = plt.subplots(2, 2, figsize=(5, 5))\n",
    "axs[0, 0].hist(data[0])\n",
    "axs[1, 0].scatter(data[0], data[1])\n",
    "axs[0, 1].plot(data[0], data[1])\n",
    "axs[1, 1].hist2d(data[0], data[1])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
